Public Sentiment and the State Standards
Unit 3 Case Study
Dr. Shaun Kellogg
October 25, 2021
1. PREPARE
Data sources such as digital learning environments and administrative data systems, as well as data produced by social media websites and the mass digitization of academic and practitioner publications, hold enormous potential to address a range of pressing problems in education, but collecting and analyzing text-based data also presents unique challenges. This week, our case study is guided by my colleague Josh Rosenberg’s recent article, Advancing new methods for understanding public sentiment about educational reforms: The case of Twitter and the Next Generation Science Standards.
We will focus on conducting a very simplistic “replication study” by comparing the sentiment of tweets about the Next Generation Science Standards (NGSS) and Common Core State Standards (CCSS) in order to better understand public reaction to these two curriculum reform efforts. Specifically, our Unit 3 case study will cover the following topics:
- Prepare: We’ll take a quick look at Dr. Rosenberg’s study, load packages we’ll need for analysis, and learn how to use Twitter’s API to retrieve data for those that created a Twitter Developer account.
- Wrangle: We focus on basic text mining processes such as text tokenization and stop word removal. Specifically, we will learn how to “tidy text” so we can perform some basic analyses such as retrieving word counts and term frequencies.
- Explore: In order to see what insight our data provides into answering our research questions, we will calculate some basic simple summary statistics from our tidied text and use data visualization to highlight some of these insights.
- Model: We learn about sentiment lexicons in our third lab and introduce the {vader} package to model the sentiment of tweets about the NGSS and CCSS state standards in order to better understand public reaction to these two curriculum reform efforts.
Communicate:
1a. Review the Literature
The Unit 3 Case Study: Public Sentiment and the State Standards is guided by a recent publication by (Rosenberg et al., 2021) Understanding Public Sentiment About Educational Reforms: The Next Generation Science Standards on Twitter. This study in turn builds on upon previous work by Wang & Fikis (2017) examining public opinion on the Common Core State Standards (CCSS) on Twitter. For Module 1, we will focus on analyzing tweets about the Next Generation Science Standards (NGSS) and Common Core State Standards (CCSS) in order to better understand key words and phrases that emerge, as well as public sentiment towards these two curriculum reform efforts.
Abstract
System-wide educational reforms are difficult to implement in the United States, but despite the difficulties, reforms can be successful, particularly when they are associated with broad public support. This study reports on the nature of the public sentiment expressed about a nationwide science education reform effort, the Next Generation Science Standards (NGSS). Through the use of data science techniques to measure the sentiment of posts on Twitter about the NGSS (N = 565,283), we found that public sentiment about the NGSS is positive, with only 11 negative posts for every 100 positive posts. In contrast to findings from past research and public opinion polling on the Common Core State Standards, sentiment about the NGSS has become more positive over time—and was especially positive for teachers. We discuss what this positive sentiment may indicate about the success of the NGSS in light of opposition to the Common Core State Standards.
Data Sources
Similar to data we’ll be using for this case study, Rosenberg et al. used publicly accessible data from Twitter collected using the Full-Archive Twitter API and the {rtweet} package in R. Specifically, the authors accessed tweets and user information from the hashtag-based #NGSSchat online community, all tweets that included any of the following phrases, with “/” indicating an additional phrase featuring the respective plural form: “ngss,” “next generation science standard/s,” “next gen science standard/s.”
Data used in this case was pulled using an Academic Research developer account and the {academictwitter} package, which uses the Twitter API v2 endpoints and allows researchers to access the full twitter archive, unlike the standard developer account. Data includes all tweets from January through May of 2020 and included the following terms: #ccss, common core, #ngsschat, ngss. Below is an example of the code used to retrieve data for this lab. This code is set not to execute and will not run, but it does illustrate the search query used, variables selected, and time frame. For those that created a standard developer account, we will learn how to use your developer account later in this section to retrieve data from Twitter.
library(academictwitteR)
library(tidyverse)
ccss_tweets_2021 <-
get_all_tweets('(#commoncore OR "common core") -is:retweet lang:en',
"2021-01-01T00:00:00Z",
"2021-05-31T00:00:00Z",
bearer_token,
data_path = "ccss-data/",
bind_tweets = FALSE)
ccss_tweets <- bind_tweet_jsons(data_path = "ccss-data/") %>%
select(text,
created_at,
author_id,
id,
conversation_id,
source,
possibly_sensitive,
in_reply_to_user_id)
write_csv(ccss_tweets, here("data", "ccss-tweets.csv"))Analysis
The authors determined Tweet sentiment using the Java version of SentiStrength to assign tweets to two 5-point scales of sentiment, one for positivity and one for negativity, because SentiStrength is a validated measure for sentiment in short informal texts (Thelwall et al., 2011). In addition, they used this tool because Wang and Fikis (2019) used it to explore the sentiment of CCSS-related posts. We’ll be using the AFINN sentiment lexicon which also assigns words in a tweet to two 5-point scales, in addition to exploring some other sentiment lexicons to see if they produce similar results. We will use a similar approach to label tweets as positive, negative, or neutral using the {Vader} package which greatly simplifies this process.
The authors also used the lme4 package in R to run a mixed effects model to determine if sentiment changes over time and differs between teachers and non-teachers. We won’t try to replicate in this study, but we will take a look at some of their findings from this model in below.
Summary of Key Findings
- Contrasting with sentiment about CSSS, sentiment about the NGSS science education reform effort is overwhelmingly positive, with approximately 9 positive tweets for every negative tweet.
- Teachers were more positive than non-teachers, and sentiment became substantially more positive over the ten years of NGSS-related posts.
- Differences between the context of the tweets were small, but those that did not include the #NGSSchat hashtag became more positive over time than those posts that did not include the hashtag.
- Individuals posted more tweets during #NGSSchat chats, the sentiment of their posts was more positive, suggesting that while the context of individual tweets has a small effect (with posts not including the hashtag becoming more positive over time), the effect upon individuals of being involved in the #NGSSchat was positive.
Finally, you can watch Dr. Rosenberg provide a quick 3-minute overview of this work at <https://stanford.app.box.com/s/i5ixkj2b8dyy8q5j9o5ww4nafznb497x>
1b. Define Questions
One overarching question that Silge and Robinson (2018) identify as a central question to text mining and natural language processing, and that we’ll explore later in this case study, is the question:
How do we to quantify what a document or collection of documents is about?
The questions guiding the Rosenberg et al. study attempt to quantify public sentiment around the NGSS and how that sentiment changes over time. Specifically, they asked:
- What is the public sentiment expressed toward the NGSS?
- How does sentiment for teachers differ from non-teachers?
- How do tweets posted to #NGSSchat differ from those without the hashtag?
- How does participation in #NGSSchat relate to the public sentiment individuals express?
- How does public sentiment vary over time?
For our text mining case study, we’ll use approaches similar to those used by the authors cited above to better understand public discourse surrounding these standards, particularly as they relate to STEM education. We will also try to guage public sentiment around the NGSS, by comparing how much more positive or negative NGSS tweets are relative to CSSS tweets. Specifically, in this case study we’ll attempt to answer the following questions:
- What are the most frequent words or phrases used in reference to tweets about the CCSS and NGSS?
- How does sentiment for NGSS compare to sentiment for CCSS?
1c. Load Libraries
tidytext 📦
As we’ll learn first hand in this module, using tidy data principles can also make many text mining tasks easier, more effective, and consistent with tools already in wide use. The {tidytext} package helps to convert text into data frames of individual words, making it easy to to manipulate, summarize, and visualize text using using familiar functions form the {tidyverse} collection of packages.
Let’s go ahead and load the {tidytext} package:
library(tidytext)For a more comprehensive introduction to the tidytext package, I cannot recommend enough the free and excellent online book, Text Mining with R: A Tidy Approach (Silge & Robinson, 2017). If you’re interested in pursuing text analysis using R post Summer Workshop, this will be a go to reference.
The vader Package 📦
The {vader} package is for the Valence Aware Dictionary for sEntiment Reasoning (VADER), a rule-based model for general sentiment analysis of social media text and specifically attuned to measuring sentiment in microblog-like contexts.
To learn more about the {vader} package and its development, take a look at the article by Hutto and Gilbert (2014), VADER: A Parsimonious Rule-based Model forSentiment Analysis of Social Media Text.
Let’s go ahead and load the VADER library:
library(vader)Note: The {vader} package can take quite some time to run on a large datasets like the one we’ll be working with, so in our Model section we will examine just a small(ish) subset of tweets.
rtweet 📦 (Optional)
The {rtweet} package provides users a range of functions designed to extract data from Twitter’s REST and streaming APIs and has three main goals:
Formulate and send requests to Twitter’s REST and stream APIs.
Retrieve and iterate over returned data.
Wrangling data into tidy structures.
For those that created a Twitter Developer account, load the {rtweet} package that we’ll be using to accomplish all three of the goals listed above:
library(rtweet)Other Packages
Finally, there are a few other packages we’ll need to get started. The first two should look familiar while third {wordcloud2} package is handy little package for creating interactive word clouds.
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.3 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 2.0.1 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x purrr::flatten() masks rtweet::flatten()
## x dplyr::lag() masks stats::lag()library(here)## here() starts at /Volumes/GoogleDrive/My Drive/College of Ed/Learning Analytics/Courses/ECI 586 Intro to LA/GitHub/eci-586library(wordcloud2)2. WRANGLE
The importance of data wrangling, particularly when working with text, is difficult to overstate. Just as a refresher, wrangling involves the initial steps of going from raw data to a dataset that can be explored and modeled (Krumm et al., 2018). This case study will place a heavy emphasis on preparing text for analysis and in particular we’ll learn how to:
- Import Tweets. First we revisit the familiar
read_csv()function for reading in our CCSS and NGSS tweets into R. For those of you who created a Twitter Developer Account, we’ll also demonstrate the use of the {rtweet} package for downloading data directly from Twitter. - Restructure Data. We focus on removing extraneous data using the
select()andfilter()functions from {dplyr}, and revisit functions from the Tidy Your Data Primer for merging data frames. - Tidy Text. Finally, we introduce the {tidytext} package to “tidy” and tokenize our tweets in order to create our data frame for analysis. We also introduce a new join function to remove “stop words” that don’t add much value to our analysis.
2a. Part 1: Import Tweets from API (Optional)
This section is optional and for those those setup a Twitter Developer Account and App. The Import Tweets section introduces the following functions from the {rtweet} package for reading Twitter data into R:
create_token()Sends request to generate authorization tokens for use of the Twitter API.search_tweets()Pulls up to 18,000 tweets from the last 6-9 days matching provided search terms.search_tweets2()Returns data from multiple search queries.get_timelines()Returns up to 3,200 tweets of one or more specified Twitter users.
Authorization methods
Users can create their personal Twitter token in two different ways. THe recommend method is outlined below.
- Navigate to developer.twitter.com/en/apps and select your Twitter app
- Click the tab labeled
Keys and tokensto retrieve your keys. - Locate the
Consumer API keys(aka “API Secret”).
- Scroll down to
Access token & access token secretand clickCreate
- Copy and paste the four keys (along with the name of your app) into an R script file and pass them along to
create_token(). Note, these keys are named secret for a reason. I recommend setting up your token in a separate R script than the one that you will eventually share.
## store api keys (these are fake example values; replace with your own keys)
app_name <- "Text Mining in Education"
api_key <- "afYS4vbIlPAj096E60c4W1fiK"
api_secret_key <- "bI91kqnqFoNCrZFbsjAWHD4gJ91LQAhdCJXCj3yscfuULtNkuu"
access_token <- "9551451262-wK2EmA942kxZYIwa5LMKZoQA4Xc2uyIiEwu2YXL"
access_token_secret <- "9vpiSGKg1fIPQtxc5d5ESiFlZQpfbknEN1f1m2xe5byw7"
## authenticate via web browser
token <- create_token(
app = app_name,
consumer_key = api_key,
consumer_secret = api_secret_key,
access_token = access_token,
access_secret = access_token_secret)Search Tweets
Since one of our goals for this walkthrough is a very crude replication of the study by Rosenberg et al. (2021), let’s begin by introducing the search_tweets() function to try reading into R 5,000 tweets containing the NGSS hashtag and store as a new data frame ngss_all_tweets.
Type or copy the following code into your R script or console and run:
ngss_all_tweets <- search_tweets(q = "#NGSSchat", n=5000)Note that the first argument q = that the search_tweets() function expects is the search term included in quotation marks and that n = specifies the maximum number of tweets
Your Turn ⤵
View your new ngss_all_tweetsdata frame using the code chunk below and answer the following questions:
ngss_all_tweets## # A tibble: 280 × 90
## user_id status_id created_at screen_name text source
## <chr> <chr> <dttm> <chr> <chr> <chr>
## 1 18373946 145269391… 2021-10-25 17:49:53 fleming77 "Educa… Tweet…
## 2 1051203741255229440 145269353… 2021-10-25 17:48:24 eva3034385… "Educa… Twitt…
## 3 46473066 145269307… 2021-10-25 17:46:33 HumanOrigi… "Educa… Twitt…
## 4 824749774830260224 145269298… 2021-10-25 17:46:11 MarcusMark… "We ha… Twitt…
## 5 120234012 145269033… 2021-10-25 17:35:41 haverlycm "I is … Twitt…
## 6 14207863 145265779… 2021-10-25 15:26:22 NMNH "Educa… Twitt…
## 7 1306383429848662016 145265653… 2021-10-25 15:21:20 LabVoces "I is … Twitt…
## 8 625111713 145265291… 2021-10-25 15:06:59 angelawwebb "I is … Twitt…
## 9 991031743179251712 145264800… 2021-10-25 14:47:27 CiresEO "PLS R… Twitt…
## 10 184649645 145264212… 2021-10-25 14:24:06 philiplbell "I is … Twitt…
## # … with 270 more rows, and 84 more variables: display_text_width <dbl>,
## # reply_to_status_id <chr>, reply_to_user_id <chr>,
## # reply_to_screen_name <chr>, is_quote <lgl>, is_retweet <lgl>,
## # favorite_count <int>, retweet_count <int>, quote_count <int>,
## # reply_count <int>, hashtags <list>, symbols <list>, urls_url <list>,
## # urls_t.co <list>, urls_expanded_url <list>, media_url <list>,
## # media_t.co <list>, media_expanded_url <list>, media_type <list>, …How many tweets did our query using the Twitter API actually return? How many variables?
Why do you think our query pulled in far less than 5,000 tweets requested?
Does our query also include retweets? How do you know?
Remove Retweets
While not explicitly mentioned in the paper, it’s likely the authors removed retweets in their query since a retweet is simply someone else reposting someone else’s tweet and would duplicate the exact same content of the original.
Let’s use the include_rts = argument to remove any retweets by setting it to FALSE:
ngss_non_retweets <- search_tweets("#NGSSchat",
n=5000,
include_rts = FALSE)Using the OR Operator
If you recall from the Data Sources section above, the authors accessed tweets and user information from the hashtag-based #NGSSchat online community, all tweets that included any of the following phrases, with “/” indicating an additional phrase featuring the respective plural form: “ngss,” “next generation science standard/s,” “next gen science standard/s.”
Let’s modify our query using the OR operator to also include “ngss” so it will return tweets containing either #NGSSchat or “ngss” and assign to ngss_or_tweets:
ngss_or_tweets <- search_tweets(q = "#NGSSchat OR ngss",
n=5000,
include_rts = FALSE)Your Turn ⤵
Try including both search terms but excluding the OR operator to answer the following question:
ngss_both_tweets <- search_tweets(q = "#NGSSchat ngss",
n=5000,
include_rts = FALSE)Does excluding the
ORoperator return more tweets, the same number of tweets, or fewer tweets? Why?What other useful arguments does the
search_tweet()function contain? Try adding one and see what happens.
Hint: Use the ?search_tweets help function to learn more about the q argument and other arguments for composing search queries.
Use Multiple Queries
Unfortunately, the OR operator will only get us so far. In order to include the additional search terms, we will need to use the c() function to combine our search terms into a single list.
The rtweets package has an additional search_tweets2() function for using multiple queries in a search. To do this, either wrap single quotes around a search query using double quotes, e.g., q = '"next gen science standard"' or escape each internal double quote with a single backslash, e.g., q = "\"next gen science standard\"".
Copy and past the following code to store the results of our query in ngss_tweets:
ngss_tweets <- search_tweets2(c("#NGSSchat OR ngss",
'"next generation science standard"',
'"next generation science standards"',
'"next gen science standard"',
'"next gen science standards"' ),
n=5000,
include_rts = FALSE)Our First Dictionary
Recall that for our research question we wanted to compare public sentiment about both the NGSS and CCSS state standards. Let’s go ahead and create our very first “dictionary” for identifying tweets related to either set of standards, and then use that dictionary for our the q = query argument to pull tweets related to the state standards.
To do so, we’ll need to add some additional search terms to our list:
ngss_dictionary <- c("#NGSSchat OR ngss",
'"next generation science standard"',
'"next generation science standards"',
'"next gen science standard"',
'"next gen science standards"')
ngss_tweets <- search_tweets2(ngss_dictionary, n=5000, include_rts = FALSE)Now let’s create a dictionary for the Common Core State Standards and pass that to our search_tweets() function to get the most recent tweets:
ccss_dictionary <- c("#commoncore", '"common core"')
ccss_tweets <-
ccss_dictionary %>%
search_tweets2(n=5000, include_rts = FALSE)Notice that you can use the pipe operator with the search_tweets() function just like you would other functions from the tidyverse.
Your Turn ⤵
- Use the
search_tweetsfunction to create you own custom query for a twitter hashtag or topic(s) of interest.
Other Useful Queries
For your independent analysis, you may be interest in exploring posts by specific users rather than topics, key words, or hashtags. Yes, there is a function for that too!
For example, let’s create another list containing the usernames of me and some of my colleagues at the Friday Institute using the c() function again and use the get_timelines() function to get the most recent tweets from each of those users:
fi <- c("sbkellogg", "TooSweetGeek", "haspires", "tarheel93", "drcallie_tweets", "AlexDreier")
fi_tweets <- fi %>% get_timelines(include_rts=FALSE)And let’s use the sample_n() function from the dplyr package to pick 10 random tweets and use select() to select and view just the screenname and text columns that contains the user and the content of their post:
sample_n(fi_tweets, 10) %>% select(screen_name, text)## # A tibble: 10 × 2
## screen_name text
## <chr> <chr>
## 1 haspires "Join us for this important conversation! Register at https:…
## 2 AlexDreier "The responses to the Nicki Minaj vaccine tweet are everythi…
## 3 AlexDreier "They say a big part of parenting is showing up and I sure h…
## 4 drcallie_tweets "Love this thread! 👏🏾👏🏾👏🏾 Congratulations! 🥳 #FailFor…
## 5 haspires "Mark your calendar to join us Nov. 18 to honor the incompar…
## 6 AlexDreier "Apparently my son decided to start drawing a chess board wi…
## 7 TooSweetGeek "ALL 👏 OF 👏 THIS 👏\n\nMovies typically get diabetes repre…
## 8 TooSweetGeek "@Sea_likedaocean eh huh... eh huh... I understood the assig…
## 9 haspires "Tune in to hear Dr. Melissa Rasberry discuss 21st century s…
## 10 haspires "@DrJackson06 @Dr_Rob_Jackson @SuperTCS @DrTawannahAllen @Do…We’ve only scratched the surface of the number of functions available in the rtweets package for searching Twitter. Use the following function to learn more about the {retwee} package:
vignette("intro", package="rtweet")Your Turn ⤵
To conclude Section 2a, try one of the following search functions from the rtweet vignette:
get_timelines()Get the most recent 3,200 tweets from users.stream_tweets()Randomly sample (approximately 1%) from the live stream of all tweets.get_friends()Retrieve a list of all the accounts a user follows.get_followers()Retrieve a list of the accounts following a user.get_favorites()Get the most recently favorited statuses by a user.get_trends()Discover what’s currently trending in a city.search_users()Search for 1,000 users with the specific hashtag in their profile bios.
2a. Part 2: Import Tweets from CSV
First, let’s use the by now familiar read_csv() and here() functions to import our ccss_tweets.csv file saved in our data folder:
ccss_tweets <- read_csv(here("unit-3", "data", "ccss-tweets.csv"),
col_types = cols(author_id = col_character(),
id = col_character(),
conversation_id = col_character(),
in_reply_to_user_id = col_character()
)
)## Warning: One or more parsing issues, see `problems()` for detailsNote the addition of the col_types = argument for changing some of the column types to character strings because the numbers for those particular columns actually indicate identifiers for authors and tweets:
author_id= the author of the tweetid= the unique id for each tweetconverastion_id= the unique id for each conversation threadin_reply_to_user_id= the author of the tweet being replied to
Your Turn ⤵
Use the following code chunk to import the NGSS tweets located in the same data folder as our common core tweets. By default, R will treat numerical IDs in our dataset as numeric values but we will need to convert these to characters like demonstrated above for the purpose of analysis:
ngss_tweets <- read_csv(here("unit-3", "data", "ngss-tweets.csv"),
col_types = cols(author_id = col_character(),
id = col_character(),
conversation_id = col_character(),
in_reply_to_user_id = col_character()
)
)Importing data and dealing with data types can be a bit tricky, especially for beginners. Recall from previous case studies that RStudio has an “Import Dataset” feature in the Environment Pane that can help you use the {readr} package and associated functions to greatly facilitate this process. If you get stuck, you can copy the code generated in the lower right hand corner of the Import Dataset window.
Now use the following code chunk to inspect the head() of each data frame and answer the questions that follow:
head(ngss_tweets)## # A tibble: 6 × 8
## text created_at author_id id conversation_id source
## <chr> <dttm> <chr> <chr> <chr> <chr>
## 1 "Please help u… 2021-01-06 00:50:49 3279907796 13466… 13466201998945… Twitte…
## 2 "What lab mate… 2021-01-06 00:45:32 1010324664… 13466… 13466188701325… Hootsu…
## 3 "I recently sa… 2021-01-06 00:39:37 61829645 13466… 13466173820858… Twitte…
## 4 "I'm thrilled … 2021-01-06 00:30:13 461653415 13466… 13466150172071… Twitte…
## 5 "PLS RT. Excit… 2021-01-06 00:15:05 22293234 13466… 13466112069671… Twitte…
## 6 "Inspired by M… 2021-01-06 00:00:00 3317960226 13466… 13466074140999… TweetD…
## # … with 2 more variables: possibly_sensitive <lgl>, in_reply_to_user_id <chr>head(ccss_tweets)## # A tibble: 6 × 8
## text created_at author_id id conversation_id source
## <chr> <dttm> <chr> <chr> <chr> <chr>
## 1 "@catturd2 Hmm… 2021-01-02 00:49:28 1609854356 13451… 13451697062071… Twitte…
## 2 "@homebrew1500… 2021-01-02 00:40:05 1249594897… 13451… 13451533915976… Twitte…
## 3 "@ClayTravis D… 2021-01-02 00:32:46 8877070540… 13451… 13450258639942… Twitte…
## 4 "@KarenGunby @… 2021-01-02 00:24:01 1249594897… 13451… 13451533915976… Twitte…
## 5 "@keith3048 I … 2021-01-02 00:23:42 1252747591 13451… 13451533915976… Twitte…
## 6 "Probably comm… 2021-01-02 00:18:38 1276017320… 13451… 13451625486818… Twitte…
## # … with 2 more variables: possibly_sensitive <lgl>, in_reply_to_user_id <chr>Wow, so much for a family friendly case study! Based on this very limited sample, which set of standards do you think Twitter users are more negative about?
- CSSS
Let’s take a slightly larger sample of the CCSS tweets:
ccss_tweets %>%
sample_n(20) %>%
relocate(text)## # A tibble: 20 × 8
## text created_at author_id id conversation_id source
## <chr> <dttm> <chr> <chr> <chr> <chr>
## 1 "@thehill 20%… 2021-01-19 15:31:35 1330242578… 13515… 13514014888331… Twitte…
## 2 "@sammons_jac… 2021-04-19 15:29:46 9968241136… 13841… 13841437775944… Twitte…
## 3 "@ThorDeplora… 2021-01-13 14:24:03 7893025195… 13493… 13493519597913… Twitte…
## 4 "@SlimJim I g… 2021-05-22 01:42:55 4925351871 13959… 13959126199455… Twitte…
## 5 "Doge has alr… 2021-04-17 00:36:34 1351679458… 13832… 13832177892934… Twitte…
## 6 "@jnewman1215… 2021-02-17 13:47:33 1165828683… 13620… 13618144638134… Twitte…
## 7 "@1DanLawson … 2021-01-07 02:42:56 9089095379… 13470… 13470086433178… Twitte…
## 8 "\"They\" do … 2021-01-13 22:06:13 1285425356… 13494… 13494778791727… Twitte…
## 9 "Brainwashing… 2021-04-19 23:20:34 1329195462… 13842… 13842858257566… FS Pos…
## 10 "@Valleykat2 … 2021-02-01 02:48:02 1074400529… 13560… 13560421142959… Twitte…
## 11 "Common Core … 2021-02-09 18:52:32 192891390 13592… 13592136123053… Twitte…
## 12 "@daliz @Chri… 2021-04-09 17:29:17 40380284 13805… 13744130726815… Twitte…
## 13 "catching up … 2021-04-21 14:06:40 9942769075… 13848… 13848712085928… Twitte…
## 14 "@5thBiz @lef… 2021-05-11 22:19:39 1390688115… 13922… 13922303891507… Twitte…
## 15 "Read it on P… 2021-02-18 12:40:07 22384582 13623… 13623813793932… Social…
## 16 "@honeybunO_o… 2021-03-27 03:50:41 1289715618… 13756… 13756472329140… Twitte…
## 17 "Common Core … 2021-03-06 03:18:13 1366571687… 13680… 13680381803021… Twitte…
## 18 "#commoncore … 2021-04-04 16:28:38 9170559692… 13787… 13787463433347… Twitte…
## 19 "@zehrarizvi … 2021-01-04 18:40:43 2825696400 13461… 13461556044616… Twitte…
## 20 "@snowball192… 2021-02-08 17:51:11 2324802395 13588… 13588238285505… Twitte…
## # … with 2 more variables: possibly_sensitive <lgl>, in_reply_to_user_id <chr>Your Turn ⤵
Use the code chunk below to take a sample of the NGSS tweets. Try to do it without looking at the code above first:
ngss_tweets %>%
sample_n(20) %>%
relocate(text)## # A tibble: 20 × 8
## text created_at author_id id conversation_id source
## <chr> <dttm> <chr> <chr> <chr> <chr>
## 1 "@Art_ngss I'… 2021-01-04 16:05:18 1240158649 13461… 12521627911102… GetVid…
## 2 "The interest… 2021-01-07 01:29:12 3279907796 13469… 13469922485802… Twitte…
## 3 "With NGSS al… 2021-02-05 19:59:32 1126946204… 13577… 13577809207539… Fanboo…
## 4 "@ebzscience … 2021-02-06 14:27:25 1105446665… 13580… 13575118989803… Twitte…
## 5 "Tonight at I… 2021-02-16 17:48:51 257762014 13617… 13617343002621… Twitte…
## 6 "@NGSS_tweeps… 2021-01-27 16:42:17 1195528902 13544… 13540590132945… Twitte…
## 7 "@racheljcox8… 2021-05-21 01:42:52 2381563549 13955… 13955526336335… TweetD…
## 8 "Welcome to #… 2021-04-16 01:00:00 558971700 13828… 13828612996175… TweetD…
## 9 "My @288Eagle… 2021-05-12 14:40:25 234807932 13924… 13924898464474… Twitte…
## 10 "Thank you to… 2021-04-28 17:11:00 239932187 13874… 13874543115510… Twitte…
## 11 "@helenjwc @J… 2021-02-18 16:06:56 312851602 13624… 13591993969870… Twitte…
## 12 "Get your chi… 2021-05-10 16:01:06 184683496 13917… 13917853748973… Buffer
## 13 "Unit Plan: M… 2021-02-28 14:40:05 287671900 13660… 13660354476763… Twitte…
## 14 "A1 asking a … 2021-04-16 01:09:14 10950512 13828… 13828636218317… Twitte…
## 15 "STEMscopes N… 2021-03-16 07:29:07 1011672613… 13717… 13717251989075… IFTTT
## 16 "Are you pass… 2021-05-15 17:30:06 23679615 13936… 13936197134611… Metigy
## 17 "Further, to … 2021-02-27 05:38:29 783109033 13655… 13655345371454… Twitte…
## 18 "It’s a beaut… 2021-05-27 18:12:46 1583597490 13979… 13979791065919… Twitte…
## 19 "Q2 here on #… 2021-04-16 01:16:01 1449382200 13828… 13828653298864… TweetD…
## 20 "Kid's engage… 2021-04-04 17:30:03 184683496 13787… 13787618002635… social…
## # … with 2 more variables: possibly_sensitive <lgl>, in_reply_to_user_id <chr>Still of the same opinion?
- Yes, these appear much less negative.
What else you notice about our data sets? Record a few observations that you think are relevant to our analysis or might be useful for future analyses.
What questions do you have about these data sets? What are you still curious about?
2b. Restructure Data
Subset Tweets
As you may have noticed, we have more data than we need for our analysis and should probably pare it down to just what we’ll use.
Let’s start with the CCSS tweets first. And since this is a family friendly case study, let’s use the filter() function introduced in previous labs to filter out rows containing “possibly sensitive” language:
ccss_tweets_1 <- ccss_tweets %>%
filter(possibly_sensitive == "FALSE")Now let’s use the select() function to select the following columns from our new ss_tweets_clean data frame:
textcontaining the tweet which is our primary data source of interestauthor_idof the user who created the tweetcreated_attimestamp for examining changes in sentiment over timeconversation_idfor examining sentiment by conversationsidfor the unique reference id for each tweet and useful for counts
ccss_tweets_2 <- ccss_tweets_1 %>%
select(text,
author_id,
created_at,
conversation_id,
id)Your Turn ⤵
Note: The select() function will also reorder your columns based on the order in which you list them.
Use the code chunk below to reorder the columns to your liking and assign to ccss_tweets_3:
ccss_tweets_3 <- ccss_tweets_1 %>%
select(id,
text,
author_id,
created_at,
conversation_id)Add & Relocate Columns
Finally, since we are interested in comparing the sentiment of NGSS tweets with CSSS tweets, it would be helpful if we had a column to quickly identify the set of state standards with which each tweet is associated.
We’ll use the mutate() function to create a new variable called standards to label each tweets as “ngss”:
ccss_tweets_4 <- mutate(ccss_tweets_2, standards = "ccss")
colnames(ccss_tweets_4)## [1] "text" "author_id" "created_at" "conversation_id"
## [5] "id" "standards"And just because it bothers me, I’m going to use the relocate() function to move the standards column to the first position so I can quickly see which standards the tweet is from:
ccss_tweets_5 <- relocate(ccss_tweets_4, standards)
colnames(ccss_tweets_5)## [1] "standards" "text" "author_id" "created_at"
## [5] "conversation_id" "id"Again, we could also have used the select() function to reorder columns like so:
ccss_tweets_5 <- ccss_tweets_4 %>%
select(standards,
text,
author_id,
created_at,
conversation_id,
id)
colnames(ccss_tweets_5)## [1] "standards" "text" "author_id" "created_at"
## [5] "conversation_id" "id"Before moving on to the CCSS standards, let’s use the %>% operator and rewrite the code from our wrangling so there is less redundancy and it is easier to read:
# Search Tweets
ccss_tweets_clean <- ccss_tweets %>%
filter(possibly_sensitive == "FALSE") %>%
select(text, author_id, created_at, conversation_id, id) %>%
mutate(standards = "ccss") %>%
relocate(standards)
head(ccss_tweets_clean)## # A tibble: 6 × 6
## standards text author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 ccss "@catturd2 H… 1609854356 2021-01-02 00:49:28 13451697062071… 13451…
## 2 ccss "@homebrew15… 1249594897… 2021-01-02 00:40:05 13451533915976… 13451…
## 3 ccss "@ClayTravis… 8877070540… 2021-01-02 00:32:46 13450258639942… 13451…
## 4 ccss "@KarenGunby… 1249594897… 2021-01-02 00:24:01 13451533915976… 13451…
## 5 ccss "@keith3048 … 1252747591 2021-01-02 00:23:42 13451533915976… 13451…
## 6 ccss "Probably co… 1276017320… 2021-01-02 00:18:38 13451625486818… 13451…Your Turn ⤵
Recall from section 1b. Define Questions that we are interested in comparing word usage and public sentiment around both the Common Core and Next Gen Science Standards.
Create an new ngss_tweets_clean data frame consisting of the Next Generation Science Standards tweets we imported by using the code above as a guide.
ngss_tweets_clean <- ngss_tweets %>%
filter(possibly_sensitive == "FALSE") %>%
select(text, author_id, created_at, conversation_id, id) %>%
mutate(standards = "ngss") %>%
relocate(standards)
head(ngss_tweets_clean)## # A tibble: 6 × 6
## standards text author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 ngss "Please help… 3279907796 2021-01-06 00:50:49 13466201998945… 13466…
## 2 ngss "What lab ma… 1010324664… 2021-01-06 00:45:32 13466188701325… 13466…
## 3 ngss "I recently … 61829645 2021-01-06 00:39:37 13466173820858… 13466…
## 4 ngss "I'm thrille… 461653415 2021-01-06 00:30:13 13466150172071… 13466…
## 5 ngss "PLS RT. Exc… 22293234 2021-01-06 00:15:05 13466112069671… 13466…
## 6 ngss "Inspired by… 3317960226 2021-01-06 00:00:00 13466074140999… 13466…Merge Data Frames
Finally, let’s combine our CCSS and NGSS tweets into a single data frame by using the union() function from dplyr and simply supplying the data frames that you want to combine as arguments:
ss_tweets <- union(ccss_tweets_clean,
ngss_tweets_clean)
ss_tweets## # A tibble: 35,233 × 6
## standards text author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 ccss "@catturd2 H… 1609854356 2021-01-02 00:49:28 13451697062071… 13451…
## 2 ccss "@homebrew15… 124959489… 2021-01-02 00:40:05 13451533915976… 13451…
## 3 ccss "@ClayTravis… 887707054… 2021-01-02 00:32:46 13450258639942… 13451…
## 4 ccss "@KarenGunby… 124959489… 2021-01-02 00:24:01 13451533915976… 13451…
## 5 ccss "@keith3048 … 1252747591 2021-01-02 00:23:42 13451533915976… 13451…
## 6 ccss "Probably co… 127601732… 2021-01-02 00:18:38 13451625486818… 13451…
## 7 ccss "@LisaS4680 … 922132923… 2021-01-02 00:16:11 13451595466087… 13451…
## 8 ccss "@JerryGl291… 122016089… 2021-01-02 00:10:29 13447179758914… 13451…
## 9 ccss "@JBatNC304 … 880914489… 2021-01-02 00:09:15 13447403608625… 13451…
## 10 ccss "@chiefaugur… 124959489… 2021-01-01 23:54:38 13451533915976… 13451…
## # … with 35,223 more rowsNote that when creating a “union” like this (i.e. stacking one data frame on top of another), you should have the same number of columns in each data frame and they should be in the exact same order.
Alternatively, we could have used the bind_rows() function from {dplyr} as well:
ss_tweets <- bind_rows(ccss_tweets_clean,
ngss_tweets_clean)
ss_tweets## # A tibble: 35,233 × 6
## standards text author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 ccss "@catturd2 H… 1609854356 2021-01-02 00:49:28 13451697062071… 13451…
## 2 ccss "@homebrew15… 124959489… 2021-01-02 00:40:05 13451533915976… 13451…
## 3 ccss "@ClayTravis… 887707054… 2021-01-02 00:32:46 13450258639942… 13451…
## 4 ccss "@KarenGunby… 124959489… 2021-01-02 00:24:01 13451533915976… 13451…
## 5 ccss "@keith3048 … 1252747591 2021-01-02 00:23:42 13451533915976… 13451…
## 6 ccss "Probably co… 127601732… 2021-01-02 00:18:38 13451625486818… 13451…
## 7 ccss "@LisaS4680 … 922132923… 2021-01-02 00:16:11 13451595466087… 13451…
## 8 ccss "@JerryGl291… 122016089… 2021-01-02 00:10:29 13447179758914… 13451…
## 9 ccss "@JBatNC304 … 880914489… 2021-01-02 00:09:15 13447403608625… 13451…
## 10 ccss "@chiefaugur… 124959489… 2021-01-01 23:54:38 13451533915976… 13451…
## # … with 35,223 more rowsThe distinction between these two functions is that union by default removes any duplicate rows that might have shown up in our queries.
However, since both functions returned the same number of rows, it’s clear we do not have any duplicates. If we wanted to verify, {dplyr} also has an intersect function to merge the two data frames, but only where they intersect(), or where they have duplicate rows.
ss_tweets_duplicate <- intersect(ccss_tweets_clean,
ngss_tweets_clean)Your Turn ⤵
Finally, let’s take a quick look at both the head() and the tail() of this new ss_tweets data frame to make sure it contains both “ngss” and “ccss” standards:
head(ss_tweets)## # A tibble: 6 × 6
## standards text author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 ccss "@catturd2 H… 1609854356 2021-01-02 00:49:28 13451697062071… 13451…
## 2 ccss "@homebrew15… 1249594897… 2021-01-02 00:40:05 13451533915976… 13451…
## 3 ccss "@ClayTravis… 8877070540… 2021-01-02 00:32:46 13450258639942… 13451…
## 4 ccss "@KarenGunby… 1249594897… 2021-01-02 00:24:01 13451533915976… 13451…
## 5 ccss "@keith3048 … 1252747591 2021-01-02 00:23:42 13451533915976… 13451…
## 6 ccss "Probably co… 1276017320… 2021-01-02 00:18:38 13451625486818… 13451…tail(ss_tweets)## # A tibble: 6 × 6
## standards text author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 ngss @BK3DSci Bria… 558971700 2021-05-21 01:10:28 13955471161272… 13955…
## 2 ngss A1 My studen… 1449382200 2021-05-21 01:10:20 13955474728990… 13955…
## 3 ngss A1: It is an … 136014942 2021-05-21 01:09:58 13955473807585… 13955…
## 4 ngss @MsB_Reilly M… 3164721571 2021-05-21 01:09:54 13955471085775… 13955…
## 5 ngss A1.5 I also l… 14449947 2021-05-21 01:09:46 13955473306029… 13955…
## 6 ngss @MsB_Reilly W… 558971700 2021-05-21 01:09:44 13955471085775… 13955…2c. Tidy Text
Text data by it’s very nature is ESPECIALLY untidy and is sometimes referred to as “unstructured” data. In this section we learn some very useful functions from the {tidytext} package to convert text to and from tidy formats. Having our text in a tidy format will allow us to switch seamlessly between tidy tools and existing text mining packages, while also making it easier to visualize text summaries in other data analysis tools like Tableau.
Tokenize Text
In Chapter 1 of Text Mining with R, Silge & Robinson (2017) define the tidy text format as a table with one-token-per-row, and explain that:
A token is a meaningful unit of text, such as a word, two-word phrase (bigram), or sentence that we are interested in using for analysis. And tokenization is the process of splitting text into tokens.
This one-token-per-row structure is in contrast to the ways text is often stored for text analysis, perhaps as strings in a corpus object or in a document-term matrix. For tidy text mining, the token that is stored in each row is most often a single word, but can also be an n-gram, sentence, or paragraph.
For this part of our workflow, our goal is to transform our ss_tweets data from this:
head(relocate(ss_tweets, text))## # A tibble: 6 × 6
## text standards author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 "@catturd2 H… ccss 1609854356 2021-01-02 00:49:28 13451697062071… 13451…
## 2 "@homebrew15… ccss 1249594897… 2021-01-02 00:40:05 13451533915976… 13451…
## 3 "@ClayTravis… ccss 8877070540… 2021-01-02 00:32:46 13450258639942… 13451…
## 4 "@KarenGunby… ccss 1249594897… 2021-01-02 00:24:01 13451533915976… 13451…
## 5 "@keith3048 … ccss 1252747591 2021-01-02 00:23:42 13451533915976… 13451…
## 6 "Probably co… ccss 1276017320… 2021-01-02 00:18:38 13451625486818… 13451…Into a “tidy text” one-token-per-row format that looks like this:
tidy_tweets <- ss_tweets %>%
unnest_tokens(output = word,
input = text) %>%
relocate(word)
head(tidy_tweets)## # A tibble: 6 × 6
## word standards author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 catturd2 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…
## 2 hmmmm ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…
## 3 common ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…
## 4 core ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…
## 5 math ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…
## 6 now ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…If you take ECI 588: Text Mining in Education, you’ll learn about other data structures for text analysis like the document-term matrix and corpus objects. For now, however, working with the familiar tidy data frame allows us to take advantage of popular packages that use the shared tidyverse syntax and principles for wrangling, exploring, and modeling data.
As demonstrated above, the tidytext package provides the incredibly powerful unnest_tokens() function to tokenize text (including tweets!) and convert them to a one-token-per-row format.
Let’s tokenize our tweets by using this function to split each tweet into a single row to make it easier to analyze and take a look:
ss_tokens <- unnest_tokens(ss_tweets,
output = word,
input = text)
head(relocate(ss_tokens, word))## # A tibble: 6 × 6
## word standards author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 catturd2 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…
## 2 hmmmm ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…
## 3 common ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…
## 4 core ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…
## 5 math ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…
## 6 now ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…There is A LOT to unpack with this function:
- First notice that
unnest_tokens()expects a data frame as the first argument, followed by two column names. - The next argument is an output column name that doesn’t currently exist but will be created as the text is “unnested” into it,
wordin this case). - This is followed by the input column that the text comes from, which we uncreatively named
text. - By default, a token is an individual word or unigram.
- Other columns, such as
author_idandcreated_at, are retained. - All punctuation has been removed.
- Tokens have been changed to lowercase, which makes them easier to compare or combine with other datasets (use the
to_lower = FALSEargument to turn off if desired).
Note: Since {tidytext} follows tidy data principles, we also could have used the %>% operator to pass our data frame to the unnest_tokens() function like so:
ss_tokens <- ss_tweets %>%
unnest_tokens(output = word,
input = text)
ss_tokens## # A tibble: 911,173 × 6
## standards author_id created_at conversation_id id word
## <chr> <chr> <dttm> <chr> <chr> <chr>
## 1 ccss 1609854356 2021-01-02 00:49:28 134516970620710… 13451703… cattur…
## 2 ccss 1609854356 2021-01-02 00:49:28 134516970620710… 13451703… hmmmm
## 3 ccss 1609854356 2021-01-02 00:49:28 134516970620710… 13451703… common
## 4 ccss 1609854356 2021-01-02 00:49:28 134516970620710… 13451703… core
## 5 ccss 1609854356 2021-01-02 00:49:28 134516970620710… 13451703… math
## 6 ccss 1609854356 2021-01-02 00:49:28 134516970620710… 13451703… now
## 7 ccss 1609854356 2021-01-02 00:49:28 134516970620710… 13451703… makes
## 8 ccss 1609854356 2021-01-02 00:49:28 134516970620710… 13451703… sense
## 9 ccss 12495948971… 2021-01-02 00:40:05 134515339159767… 13451679… homebr…
## 10 ccss 12495948971… 2021-01-02 00:40:05 134515339159767… 13451679… i
## # … with 911,163 more rowsYour Turn ⤵
The unnest_tokens() function also has a specialized “tweets” tokenizer in the tokens = argument that is very useful for dealing with Twitter text. It retains hashtags and mentions of usernames with the @ symbol as illustrated by our @catturd2 friend who featured prominently in our the first CCSS tweet.
Rewrite the code above (you can check answer below) to include the token argument set to “tweets,” assign to ss_tokens_1, and answer the questions that follow:
ss_tokens_1 <- unnest_tokens(ss_tweets,
output = word,
input = text,
token = "tweets")## Using `to_lower = TRUE` with `token = 'tweets'` may not preserve URLs.head(ss_tokens_1)## # A tibble: 6 × 6
## standards author_id created_at conversation_id id word
## <chr> <chr> <dttm> <chr> <chr> <chr>
## 1 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… @catt…
## 2 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… hmmmm
## 3 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… common
## 4 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… core
## 5 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… math
## 6 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… nowHow many observations were our original
ss_tweetsdata frame?How many observations are there now? Why the difference?
Before we move any further let’s take a quick look at the most common word in our two datasets. To do so, we’ll introduce the easy to use count() function from the {dplyr} package.
Like most functions we’ve introduced, the first argument count() expects is a data frame which we provided with the %>% operator, followed but the column, in our case word, whose values we want to count:
ss_tokens_1 %>%
count(word, sort = TRUE)## # A tibble: 74,235 × 2
## word n
## <chr> <int>
## 1 common 26665
## 2 core 26470
## 3 the 25818
## 4 to 20478
## 5 and 15552
## 6 of 13106
## 7 a 12472
## 8 math 11788
## 9 is 11562
## 10 in 10076
## # … with 74,225 more rowsWell, many of these tweets are clearly about the CCSS and math at least, but beyond that it’s a bit hard to tell because there are so many “stop words” like “the,” “to,” “and,” “in” that don’t carry much meaning by themselves.
Remove Stop Words
Often in text analysis, we will want to remove these stop words if they are not useful for an analysis. The stop_words dataset in the {tidytext} package contains stop words from three lexicons. We can use them all together, as we have here, or filter() to only use one set of stop words if that is more appropriate for a certain analysis.
Let’s take a closer the lexicons and stop words included in each:
View(stop_words)The anti_join Function
In order to remove these stop words, we will use a function called anti_join() that looks for matching values in a specific column from two datasets and returns rows from the original dataset that have no matches like so:
For a good overview of the different dplyr joins see here: https://medium.com/the-codehub/beginners-guide-to-using-joins-in-r-682fc9b1f119.
Now let’s remove stop words that don’t help us learn much about what people are saying about the state standards.
ss_tokens_2 <- anti_join(ss_tokens_1,
stop_words,
by = "word")
head(ss_tokens_2)## # A tibble: 6 × 6
## standards author_id created_at conversation_id id word
## <chr> <chr> <dttm> <chr> <chr> <chr>
## 1 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… @catt…
## 2 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… hmmmm
## 3 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… common
## 4 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… core
## 5 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… math
## 6 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… makesNotice that we’ve specified the by = argument to look for matching words in the word column for both data sets and remove any rows from the tweet_tokens dataset that match the stop_words dataset. Remember when we first tokenized our dataset I conveniently chose output = word as the column name because it matches the column name word in the stop_words dataset contained in the tidytext package. This makes our call to anti_join()simpler because anti_join() knows to look for the column named word in each dataset. However this wasn’t really necessary since word is the only matching column name in both datasets and it would have matched those columns by default.
Your Turn ⤵
Use the code chunk below to take a quick count of the most common tokens in our ss_tweets_2 data frame to see if the results are a little more meaningful, then answer the questions that follow.
ss_tokens_2 %>%
count(word, sort = TRUE)## # A tibble: 73,596 × 2
## word n
## <chr> <int>
## 1 common 26665
## 2 core 26470
## 3 math 11788
## 4 #ngsschat 3059
## 5 amp 2904
## 6 #ngss 2655
## 7 students 2559
## 8 science 2300
## 9 standards 2273
## 10 education 2174
## # … with 73,586 more rowsYour Turn ⤵
How many unique tokens are in our data tidied text?
- 73,596
How many times does the word “math” occur in our set of tweets?
- 11,788
Custom Stop Words
Notice that the nonsense word “amp” is among our high frequency words as well as some. We can create our own custom stop word list to to weed out any additional words that don’t carry much meaning but skew our data by being so prominent.
Let’s create a custom stop word list by using the simple c() function to combine our words. We can the add a filter to keep rows where words in our word column do NOT ! match words %in% my_stopwords list:
my_stopwords <- c("amp", "=", "+")
ss_tokens_3 <-
ss_tokens_2 %>%
filter(!word %in% my_stopwords)Let’s take a look at our top words again and see if that did the trick:
ss_tokens_3 %>%
count(word, sort = TRUE)## # A tibble: 73,593 × 2
## word n
## <chr> <int>
## 1 common 26665
## 2 core 26470
## 3 math 11788
## 4 #ngsschat 3059
## 5 #ngss 2655
## 6 students 2559
## 7 science 2300
## 8 standards 2273
## 9 education 2174
## 10 school 2154
## # … with 73,583 more rowsMuch better! Note that we could extend this stop word list indefinitely. Feel free to use the code chunk below to try adding more words to our stop list.
Before we move any further, let’s save our tidied tweets as a new data frame for Section 3 and also save it as a .csv file in our data folder:
ss_tidy_tweets <- ss_tokens_3
write_csv(ss_tokens_3, here("unit-3", "data", "ss_tidy_tweets.csv"))3. EXPLORE
Calculating summary statistics, data visualization, and feature engineering (the process of creating new variables from a dataset) are a key part of exploratory data analysis. For our first lab, we’re going to keep things super simple and focus on:
Top Tokens. Since once of our goals is to compare tweets about the NGSS and CSSS standards, we’ll take a look at the to 50 words that appear in each.
Word Clouds. To help illustrate the relative frequency each of these top 50 words occurs, we’ll introduce the {wordclouds2} package for creating interactive word clouds that can be knitted with your HTML doc.
3a. Top Tokens
First, let’s take advantage of the the %>% operator combine some of the functions we’ve used above with the top_n() function from the {dplyr} package. By default, this function is looking for a data frame as the first argument, and then the number of rows to return.
Let’s take a look at the top tokens among the CCSS tweets by filtering our standards by CCSS, counting the number of times each word occurs, and taking the look at the 50 most common words:
ccss_top_tokens <- ss_tidy_tweets %>%
filter(standards == "ccss") %>%
count(word, sort = TRUE) %>%
top_n(50)## Selecting by nccss_top_tokens## # A tibble: 50 × 2
## word n
## <chr> <int>
## 1 common 26599
## 2 core 26405
## 3 math 11688
## 4 education 1917
## 5 kids 1821
## 6 standards 1810
## 7 school 1806
## 8 dont 1622
## 9 grade 1443
## 10 people 1410
## # … with 40 more rowsNot surprisingly, our search terms appear in the top 50 but the word “math” also features prominently among CCSS tweets!
Word Clouds
Word clouds are much maligned and sometimes referred to as the “pie charts of text analysis,” but they can be useful for communicating simple summaries of qualitative data for education practitioners and are intuitive for them to interpret. Also, for better or worse, these are now included as a default visualization for open-ended survey items in online Qualtrics reports and you can even add your own stop words.
The {wordclouds2} package is pretty dead simple tool for generating HTML based interactive word clouds. By default, when you pass a data frame to the wordcloud2() function, it will look for a word column and a column with frequencies or counts, i.e., our column n that we created with the count() function.
Let’s run the wordcloud2() function on our ccss_top_tokens data frame.
wordcloud2(ccss_top_tokens)As you can see, “math” is a pretty common topic when discussing the common core on twitter but words like “core” and “common” – which you can see better if you click the “show in a new window” button or run the code in you console – are not very helpful since those were in our search terms when pulling data from Twitter.
In fact, search terms like these we might want to exclude from a final data product we share with with education partners or in a publication and instead include these these in a title or caption.
ccss_top_tokens %>%
filter(word != "common" & word != "core") %>%
wordcloud2()Your Turn ⤵
In the code chunk below, filter, count and select the top 50 tokens to create a word cloud for the NGSS tweets. A gold star if you can can do it without using the assignment operator or looking at the code above!
ss_tidy_tweets %>%
filter(standards == "ngss") %>%
count(word, sort = TRUE) %>%
top_n(50) %>%
wordcloud2()## Selecting by nAlso, take a look at the help file for wordclouds2 to see if there might be other ways you could improve the aesthetics of this visualization.
4. MODEL
Now that we have our tweets nice and tidy, we’re almost ready to begin exploring public sentiment (at least for the past week due to Twitter API rate limits) around the CCSS and NGSS standards. For this part of our workflow we introduce two new functions from the tidytext and dplyr packages respectively:
How do you “measure” sentiment?
Sentiment analysis tries to evaluate words for their emotional association. In Text Mining with R: A Tidy Approach, Silge aand Robinson point out that,
One way to analyze the sentiment of a text is to consider the text as a combination of its individual words and the sentiment content of the whole text as the sum of the sentiment content of the individual words.
This isn’t the only way to approach sentiment analysis, but it is an easier entry point into sentiment analysis and you’ll find that is it often-used in publications that utilize sentiment analysis.
The {tidytext} package provides access to several sentiment lexicons, sometimes referred to as dictionaries, based on unigrams, i.e., single words. These lexicons contain many English words and the words are assigned scores for positive/negative sentiment, and also possibly emotions like joy, anger, sadness, and so forth.
The three general-purpose lexicons we’ll focus on are:
AFINNassigns words with a score that runs between -5 and 5, with negative scores indicating negative sentiment and positive scores indicating positive sentiment.bingcategorizes words in a binary fashion into positive and negative categories.nrccategorizes words in a binary fashion (“yes”/“no”) into categories of positive, negative, anger, anticipation, disgust, fear, joy, sadness, surprise, and trust.
Note that if this is your first time using the AFINN and NRC lexicons, you may prompted to download both Respond yes to the prompt by entering “1” and the NRC and AFINN lexicons will download. You’ll only have to do this the first time you use the NRC lexicon.
Let’s take a quick look at each of these lexicons using the get_sentiments() function and assign them to their respective names for later use:
afinn <- get_sentiments("afinn")
afinn## # A tibble: 2,477 × 2
## word value
## <chr> <dbl>
## 1 abandon -2
## 2 abandoned -2
## 3 abandons -2
## 4 abducted -2
## 5 abduction -2
## 6 abductions -2
## 7 abhor -3
## 8 abhorred -3
## 9 abhorrent -3
## 10 abhors -3
## # … with 2,467 more rowsbing <- get_sentiments("bing")
bing## # A tibble: 6,786 × 2
## word sentiment
## <chr> <chr>
## 1 2-faces negative
## 2 abnormal negative
## 3 abolish negative
## 4 abominable negative
## 5 abominably negative
## 6 abominate negative
## 7 abomination negative
## 8 abort negative
## 9 aborted negative
## 10 aborts negative
## # … with 6,776 more rowsnrc <- get_sentiments("nrc")
nrc## # A tibble: 13,901 × 2
## word sentiment
## <chr> <chr>
## 1 abacus trust
## 2 abandon fear
## 3 abandon negative
## 4 abandon sadness
## 5 abandoned anger
## 6 abandoned fear
## 7 abandoned negative
## 8 abandoned sadness
## 9 abandonment anger
## 10 abandonment fear
## # … with 13,891 more rowsAnd just out of curiosity, let’s take a look at the loughran lexicon as well:
loughran <- get_sentiments("loughran")
loughran## # A tibble: 4,150 × 2
## word sentiment
## <chr> <chr>
## 1 abandon negative
## 2 abandoned negative
## 3 abandoning negative
## 4 abandonment negative
## 5 abandonments negative
## 6 abandons negative
## 7 abdicated negative
## 8 abdicates negative
## 9 abdicating negative
## 10 abdication negative
## # … with 4,140 more rows✅ Comprehension Check
How were these sentiment lexicons put together and validated? Hint: take a look at Chapter 2 from Text Mining with R.
Why should we be cautious when using and interpreting them?
Come to the Dark Side
As noted in the PERPARE section, the {vader} package is for the Valence Aware Dictionary for sEntiment Reasoning (VADER), a rule-based model for general sentiment analysis of social media text and specifically attuned to measuring sentiment in microblog-like contexts such as Twitter.
The VADER assigns a number of different sentiment measures based on the context of the entire social-media post or in our case a tweet. Ultimately, however, these measures are based on a sentiment lexicon similar to those you just saw above. One benefit of using VADER rather than the approaches described by Silge and Robinson is that we use it with our tweets in their original format and skip the text preprocessing steps demonstrated above.
One drawback to VADER is that it can take a little while to run since it’s computationally intensive. Instead of analyzing tens of thousands of tweets, let’s read in our original ccss-tweets.csv and take instead just a sample of 500 “untidu” CCSS tweets using the sample_n() function:
ccss_sample <- read_csv(here("unit-3", "data", "ccss-tweets.csv")) %>%
sample_n(500)## Warning: One or more parsing issues, see `problems()` for details## Rows: 27230 Columns: 8## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): text, source
## dbl (4): author_id, id, conversation_id, in_reply_to_user_id
## lgl (1): possibly_sensitive
## dttm (1): created_at##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.ccss_sample## # A tibble: 500 × 8
## text created_at author_id id conversation_id source
## <chr> <dttm> <dbl> <dbl> <dbl> <chr>
## 1 "CoMmoN COrE..… 2021-03-08 22:28:52 7.02e17 1.37e18 1.37e18 Twitte…
## 2 "Let’s revisit… 2021-05-22 00:59:34 8.28e17 1.40e18 1.40e18 Twitte…
## 3 "@VoteGloriaJ … 2021-04-23 01:27:14 1.26e 7 1.39e18 1.39e18 Twitte…
## 4 "@Tactical_rev… 2021-05-14 07:01:24 1.06e 8 1.39e18 1.39e18 Twitte…
## 5 "@Politi_Call … 2021-05-01 14:01:18 1.75e 8 1.39e18 1.39e18 Twitte…
## 6 "@Taniel @Kent… 2021-03-21 17:28:23 4.02e 8 1.37e18 1.37e18 Twitte…
## 7 "@goldenrose_7… 2021-01-14 13:00:22 1.35e18 1.35e18 1.35e18 Twitte…
## 8 "@AyannaPressl… 2021-01-13 05:38:03 7.06e17 1.35e18 1.35e18 Twitte…
## 9 "Have #Common-… 2021-01-10 21:45:28 4.07e 7 1.35e18 1.35e18 Dynami…
## 10 "Creative “ski… 2021-02-17 07:07:19 9.60e17 1.36e18 1.36e18 Twitte…
## # … with 490 more rows, and 2 more variables: possibly_sensitive <lgl>,
## # in_reply_to_user_id <dbl>Note above that we passed our read_csv() output directly to our sample() function rather than saving a new data frame object, passing that to sample_n(), and saving as another data frame object. The power of the %>% pipe!
On to the Dark Side. The {vader} package basically has just one function, vader_df() that does one thing and expects just one column from one frame. He’s very single minded! Let’s give VADER our ccss_sample data frame and include the $ operator to include only the text column containing our tweets.
vader_ccss <- vader_df(ccss_sample$text)
vader_ccss## text
## 1 CoMmoN COrE.... https://t.co/OqXXT19idk
## 2 Let’s revisit the claim the new math framework allows calculus.\n\nThe framework says no Math 1 until 9th grade then Math 2 in 10th.\n\nIn Irvine schools, where we’ve already adopted common core, a student who takes Math 2 in 10th cannot take calculus without extra summer classes. https://t.co/XvB1ke04FQ
## 3 @VoteGloriaJ @DavidDark @GovBillLee David Steiner brought in my Schwinn to review textbook adoption process...what’s he an early advocate of...Common Core.
## 4 @Tactical_review Common core math
## 5 @Politi_Call U.S. has dumbed down ppl For generations! Common Core is a reaction intended to wake up minds. Locals must create effective classwork to make it happen.\nWhy does the RIGHT refer to thinking as "dumbing down"?
## 6 @Taniel @KentOgle "In general, 22% of the state's population is Black, 70% is white and 10% is Hispanic."\n\nSomeone's common core math is showing....
## 7 @goldenrose_79 @adamscrabble Common core trains future bureaucrats
## 8 @AyannaPressley Why was Jayapal without a mask? \n\nShe now has COVID, didn’t have a mask on and others got sick, but no one is blaming her? Help me with this one because I’m struggling real hard with the Common Core logic. \n\n1 + 1 = must have been a republican. It’s the new Math. https://t.co/YQ1BjuJcKq
## 9 Have #Common-Core Standards ruined our children's #handwriting? https://t.co/KGjSwVIJW9
## 10 Creative “skimming”, common core math. One for you, then Two for me. Repeat...... 👌🏾 https://t.co/SzLj6S5w8i
## 11 @MsJR88 Is this Common Core math?
## 12 @Noahpinion Harrison Bergeron is hardly new to edu in US. But first time I’ve seen it explicitly stated at state level (CA tried to do something similar w Common Core, but more subtle - ‘everyone will be same level after our Common Core plus Boaler theory, so u can remove accelerated math’)
## 13 @swampymag Did Trump keep his promise to eliminate Common Core? Did Trump keep his promise to remove all undocumented immigrants? Did Trump keep his promise to eliminate gun-free zones at schools and military bases? Did Trump keep his promise to stop the AT&T Time Warner Merger?
## 14 #CommonCore and #Vocabulary Instruction https://t.co/TPrjOfIhoZ
## 15 @GisiAnthony Not even common core math can explain this. 😜
## 16 @DIYChad726 @Beeseey_ @brenyyaaa Common core was the dumbest shit I've ever learned. Anyways I don't remember any of it lol. https://t.co/dPzzMKSSe6
## 17 @marisarose1212 @LisaMarieBoothe 51% approval . Minus our all the ppl governor’s in blue cities killed in pure negligence.\nYour common core math doesn’t add up
## 18 Common Core Math & Civics: If you have 10 sets of cojones, do you get to send 197 steers to the sale barn? #ImpeachmentDay
## 19 We need to abandon common core and return to common sense.
## 20 @MeekPhill_ @nd4668 Common core math right there
## 21 @CurtJon84716871 @GovBillLee Only liberals want to feed and educate children isn't the W you think it is. Also we still use common core just changed the name to please morons like you. Good to know how gullible his base is. You must have been educated in the TN school system.
## 22 @RyanBrownWJOX why? Why go for 2 down 17? Is this common core coaching?
## 23 @RepKClark @TeamPelosi Must be that common core math that most parents struggle with. 🤦🏻♀️
## 24 @Kaghoegames Yup because this is already going bad, with this common core Math. Why can they leave math alone.
## 25 @QTRResearch @Nationalist_KAG uh...common core math? or this... https://t.co/i8d7oq2tR4
## 26 @K8EA You must be looking at classwork (created by textbooks) instead of standards (Common Core).
## 27 @mattfitzct Common core says otherwise, fren.
## 28 @DeAngelisCorey ANYTHING is better than public dumb down indoctrination school!!!!!! common core is the WORST!!!!
## 29 @politickingapp Did they leave a zero off? This would make more sense if it was 14,000 #commoncore #math
## 30 @heyMikeCruz @SamuelWBell This is a terrible approach: it needs paper and pencil, it involves multiple unnatural and non-obvious steps such as carrying\n\nBetter is 39+36=(40-1)+(35+1)=40+35=75 which you can do in your head. In fact, that's how I would do it, and what Common Core is intended to teach.
## 31 @livelovenofear @DianeDenizen Well the openly stated goal is 500 million people left on earth from 7.5 billion by 2025. So you do the math, unless you had common core.\nhttps://t.co/YhpzlS7Bgq
## 32 @Breaking911 This was calculated with common core math.
## 33 @thosethatcan Year 9 is Grade 9 in the US, which is the first year of High School. They call ‘English’, English Language Arts (ELA). Most (but not all) schools will use the Common Core ‘standards’ (what we call learning objectives/I can do statements). More info here:\nhttps://t.co/yXclKtJVx2
## 34 @TempletonZe @WalshFreedom Genuine dialogue is going to be tough, given the large tent: the Q-"Shaman," the Proud Boi, and the Confederate douche probably share few common, core beliefs. But Trump unites them through a long list of things to hate, and an equally long list of lies to manipulate them.
## 35 @Oilfield_Rando could lift more than 1 million out of poverty...and leave 1.3 million unemployed.\ncommon core math makes socialism sound so good
## 36 Why the Common Core standards failed — and what it means for school reform https://t.co/nxK4BMTHYG
## 37 @DeAngelisCorey @rweingarten Common Core math. You wouldn't understand.
## 38 2/Learning Cursive Isn't Required Anymore\n\nWith Technology, Is There A Need For Cursive Writing In School Now?\n\nCommon Core not requiring teaching cursive in elementary school is an abject failure in American education that'll make US less competitive. \nhttps://t.co/96swwIdA5M
## 39 In the end these are American citizens. Instead of fighting literal fascists, we fought about common core education, healthcare and how to respond to the pandemic. Where are the opportunity to solve the deltas?
## 40 @wdunlap Common core was created to create idiots
## 41 We have just the perfect solution for American kids’ deep ignorance about their nation’s founding principles, system of government, and history. It’s making them into political activists!\n\nBy @JoyPullmann\nVia @FDRLST https://t.co/y2SiRgh25Z
## 42 @joshdcaplan I guess #CocaineMitch is a common core math scholar😑😒😏 After Jan. 20th Trump ain't president, @LeaderMcConnell <<< This A Hole is one of the most corrupt scumbags in the #Senate https://t.co/HYoCR484aX
## 43 @ScottBaio Funny, doesn't look like a fox!! I hate Common Core!! I'm a retired teacher and, High School Principal!! Hate it!!
## 44 @cherokeecreek Public school curriculum has been manipulated especially by the Common Core so that many children seem to be failing. It has undermined the self-confidence of millions of children. This is why support of public schools is waning.
## 45 @TeddyRepublican @charliekirk11 Bwahaha see this is where you are wrong Sir as is your Buddy below there is no #TakeAClass or #TheyJustDecided we discuss shit here in #TheUSA we dont boss other Peoples Kids around & we dont tell each other what 2 do. \n\n😩 #CRT is #CommonCore #SSDD https://t.co/iqHA7KX69S
## 46 @news10nbc How about if we just have normal school? RCSD trying to make shit hybrid? First.. common core has got to go! 2 generations have been robbed of basic math skills! God only knows what is in today's history, oops, I mean SOCIAList studies, books! Common core =Communist core. Get it?
## 47 @catturd2 Common core. 😂
## 48 @Jmdouk @SEC_Enforcement @CEOAdam \nFuzzy Common Core level
## 49 These are common core values shared by many. List three of your core values then learn more at https://t.co/DVA09oe6wf https://t.co/Av3VOPfYzI
## 50 @GavinNewsom If 5 million vaccines have been administered how is that 1 in 10 people with a population of 40 million? Common Core Math?
## 51 @RepMattGaetz @realDonaldTrump must abolish Common Core which is nothing more than CCP's Sesame Credit!
## 52 anyways i've said it once and i'll say it again: FUCK the gt program (also get rid of common core that shit's nasty too)
## 53 @brianjoralvarez Is this common core math? 🤣🤣
## 54 @calistevenj Common core educ. dictated not teaching kids cursive writing, just dumb 🐂💩irrelevant to anything.
## 55 @dj_jecker @orvaloriginal @ChuckCallesto That fool is using common core math. Lol
## 56 @JL90394179 @ForNorton 1200+600+1400=3200. You must be using common core math 😂😂😂😂
## 57 @catturd2 I blame Common Core......
## 58 @rachelz971 Andrew was doing common core math with the casket & 6 ft under. #dgs971
## 59 Things that make you go hmmmm...\n\nIs this what the “common core math” is? Not an exact science, but yet we are supposed to follow the science?! \nI’m very confused in the libtard Candy Land world...\n\nHow is this happening??? Come on people wake up! https://t.co/JNEzpPoogW
## 60 @lynnhowlett 29 is this that common core math thing ? 🤣😎
## 61 @JoeSilverman7 Common core says you are correct!
## 62 @AaronSBlackwel1 @WickedDecent @luckybydesign @kellywchris @MastTranscript Yup...tied closer to the Common Core and also a national movement. I've listed to and read Scott Looney's work at moving MTC forward. One Stone's work is so original though...maybe it won't work out of context, but the darn thing is just beautiful. #dtk12chat
## 63 @Bored_Teachers Ever want to show students how to identify the four seasons throughout the year? Originally designed for kindergartners, this lesson can be adapted for any grade level.\n\n#commoncore #technology #seasons #googleslides #kindergarten\n\nhttps://t.co/pYtbhUc2Cs
## 64 @AEY324 @POTUS See I can even spell and type correctly. thanks amazing and wonderful common core and amazing America!
## 65 @SelenaCarrion @MoniseLSeward @MaggieEThornton @ShanaVWhite True all over private, public, primary and secondary Ed. Head start does awesome work to build numberacy and number sense as adaptable/fluid concepts and common core plus a history of thoughtless, bottom up rote algorithms as “math” just can’t take a cue from them. \n\nDepth in 🧵 https://t.co/t784GxjbC6
## 66 @BuckReising Common Core. Blame it all on common core.
## 67 Get Download Here : https://t.co/JUL2CiGNKt\n=========\nDownload PDF 3rd Grade Common Core Math: Daily Practice Workbook - Part I: Multiple Choice | 1000+ Practice Questions and Video Explanations | Argo Brothers by Argo Brothers
## 68 @QuarantinedCoof So what your saying is only big racists dislike anything common core does 🤔
## 69 @f3rnandagarciia yeah normally we’d have 6 classes (two electives + 4 common core) but this year they cut it in half (2 common core + 1 elective) so in this case, my elective is spanish
## 70 i play jumpstart, but math's hard\nand i can't stand learning anything\n i like when grown-ups call me smart\nbut i'm dumb, so i won't add sums up anymore\nand i barely even care that i'm forgetting all the common core
## 71 @tweettruth2me Delegitimizing what a gun is I guess.\n\nCommon core math❌
## 72 @catturd2 Come on, man. There was a baker's dozen showed up last time he left the basement. Common core math equals 80+ million votes. If I got $13 I'm a millionaire.
## 73 @1Ladydeadpool Common core education was the cure for common sense 😂🤣
## 74 Finally the link for the excellent analysis that's been floating around for the #abed curriculum. Curriculum was lifted from US Common Core standards (Virginia???). So not only did the UCP not consult Albertans they plagiarized even more than originally thought. #abpoli https://t.co/qgp0vy3g4o
## 75 @Tombstone1954 Biden giving a lesson in common core
## 76 @AOC @CoriBush @SenSanders @EdMarkey @chelliepingree The federal government received an estimated $3.71 trillion in tax revenue during 2020. The Green New Deal would cost $93 trillion. Was Joe using Common Core math when he said the GND would pay for itself? - Rep Boebert
## 77 @dakotanews_now I would LOVE to know how many people lost their $#!+ over the Common Core for years and are now suddenly cheering on Noem to step into our schools with her zero experience in education and her full experience with lies and propaganda.
## 78 Infiltration takes time,but it’s effective! It’s all that public education crap Common Core. Some people,like GB, are on their way to hail 2 the chief imbecile! https://t.co/hzUpZLLNQ3
## 79 Come on Man\nHere's the Deal\nBiden still figuring kickbacks off 52 EO's \nThey use common core math 2 collect10% on the DEALS, same as the #BigMan, from every check ever writen\nEach of the 3 will get 10%\nBut Warnock/Ossoff have 2 give 10% of their cut 2 the BIG MAN\nJust ask HUNTER https://t.co/m09lO9FykN https://t.co/yaRii3nM5E
## 80 @TheDemocrats @POTUS No it isn't LOL, seriously just how stupid are Democrat voters? Well after the Dept. of Labor released their jobs report showing only 250K jobs created in the first yearly quarter, that suddenly = 500K jobs being created. Must be Common Core math.
## 81 @kaylacarlileart Well, I mean your math and what he's gonna have to endure in school is whole different thing. (Common core deserves to be burned IMO)
## 82 @EthicalSkeptic Common core curriculum coupled with smart phones definitely contributed to the decline.
## 83 @kayleighmcenany Common core math.
## 84 @namatopkat @ReallyAmerican1 @chipfranklin Wait wut?! Is that common core math?
## 85 True that! As an educator I can tell you it was so sad that Trumps image, Space Force, Mars program, etc never made it to any classrooms. Bush had No Child Left Behind, Obama had Common Core-- yet Trump did not have any education legacy due to the constant attacks by leftists. https://t.co/8CuMhhTJVk
## 86 is how I got to 46%. When others drop out, I will end common core. It's a disaster.
## 87 @InsiderOilers Also the US Common Core curriculum from Virginia apparently.
## 88 @AOC You know, we could kill common core with one project:\n\nIt could be a class's senior project to determine mathematically whether it is cheaper and more cost effective to give Amazon the tax breaks they want or to use that same money give all Amazon workers basic health care.
## 89 @bradbatt The words Common Core are triggering, just white them out and add TN State Standards.
## 90 @booze_rhythm @Lancegooden Do normal math not common core
## 91 @Indigenia Don’t get your hopes up. People tend to forget that Obama had shithole secretaries of education. The only rebranded No child left behind and scores of teachers lost their benefits. Pushed charter schools and common core curriculum.
## 92 @the_brumby Hey, let me put it this way, I saw ALL of that the second he started that crap, but not like can splain it, other than this is what "equitable" looks like. Take the crappy areas, saddle the whole cnty with that, apply common core math, carry the two and sorry folks!Schools closed
## 93 [Download] Kindle Common Core Math Workbook, Grade 4: Multiple Choice, Daily Math Practice Grade 4 => https://t.co/1xTkKt7Yfy
## 94 @alexhern English the “Common Core Math” of languages.
## 95 @Citationsphy Na. Bro. That's common core math.
## 96 @jasonrantz @JackPosobiec Common Core philosophy
## 97 Why Common Core failed https://t.co/6tc4dH7Se7 via @BrookingsInst @KimberBlodgett @jmidkiff @KandyceMitchell
## 98 An analysis of "the sad adventure of Gates’ purchase of public education policy via the Common Core." @JanResseger @tomloveless99 @gatesfoundation #edpolicy https://t.co/S0JxfJONjW
## 99 YOU MIGHT BE A DEMOCRAT IF YOU TOOK COMMON CORE HISTORY HOW STUPID CAN YOU BE WHEN KAMALA HARRIS MADE BILLIONS FOR HER NAZI HANDLERS https://t.co/lh4dOKL1fZ https://t.co/bs00aSP2k1
## 100 @vikrantnyc @girlgone_crypto @FAmericanSpirit I’m in a wealthy blue state; in a super wealthy neighborhood, the public education is beyond garbage... teachers are incompetent and became teachers as most can’t do anything else... admin is retarded... the common core crap is another issue...entire US education is f$cked!
## 101 I dislike math because I am bad at it. But there is no physical limitation on my ability to do math. Its a mental block that I likely acquired because my first exposure to math was through the American "common core" system that they use in public schools
## 102 If You Liked Common Core, You're Going To Love Joe Biden's Civics https://t.co/elW9QBsOAk
## 103 @EichBomb Common core?
## 104 @ClownBasket So if you chose to watch the YouTube videos, I don't buy it all. 😸 When Common Core was developed, Dr. James Milgram - a NASA scientist - objected to it as he said it would only prepare kids for community college and not STEM programs. They removed his name from the project.
## 105 @doug_mcb @MollyJongFast Common core is the worst!
## 106 @PqmVic @kayleighmcenany No. None of us buy this common core science you want to shove down our throats. True science can be replicated over and over with the same results. Haven’t seen any to support this fear mongering. But I have seen plenty to dispute it 😉 https://t.co/UQYCFCK4Ll
## 107 @ajshanus @PecoooPowerplay @hinx Turns out I was way off. Yeah I do know a little of the old way because I started training to be an educator before common core took hold. By the time I started working in the field the band teacher had to give reading/writing assignments because reasons. https://t.co/k1yrvOfKby
## 108 @RionNile Well stupidity has been around a lot longer and is more deadly of an epidemic thanks to the public school system and common core.
## 109 Have #Common-Core Standards ruined our children's #handwriting? https://t.co/KGjSwVIJW9
## 110 @AshleyFrankly Is this some of those common core new-math numbers nonsense ⁉️ ma'am the only four you might be is out of five stars. https://t.co/biswiaxndZ
## 111 @mactavish @achievethecore One really good thing about the common core is that it doesn't dumb down curricula. Students must rise to meet it.
## 112 @StaceyCKs1 @lindyli I only tolerate the “what are you?” question at the preschool level. If kindergartners are mature enough to start learning Common Core, we can start teaching them decent ways to talk to each other.
## 113 @TheGuyNamedJoe @rising_serpent Trump gave Americans 1200 then 600. \nYou probably took common core math.
## 114 @bigsecksa @JYSexton Obviously they taught you common core math wherever you are.
## 115 @floridianism @megamandrn001 @NewProgressUSA @pseudocia @FaerieWhings @besf0rt Yeah dumb fuck, that’s a piece meal stupid way to go about it. “WE neEd bIg structural ChAnGe” but different models for each state based upon their poverty at a time. That’s stupid and exactly how we approached education for 20 years before Common Core (which has issues)
## 116 @greg_price11 @JennaEllisEsq Did Dominion help with the tabulation? Perhaps the good folks at Smartmatic helped add it up for them using that common core math.
## 117 EBOOK Download Free 7th Grade Common Core ELA (English Language Arts): Daily Practice Workbook | 300+ Practice Questions and Video Explanations | Common Core State Aligned | Argo Brothers >> https://t.co/e0yNwqc3oi
## 118 whoever came up with common core math should be put down
## 119 Divide and Conquer: An Adult Course in Common Core Division. Step-by-step guide for understanding and applying Common Core division concepts. This course contains videos teaching new ways to divide that students are learning in school today.\nhttps://t.co/W3YWh462d7\n@MaxMusicians https://t.co/GGfbxENEBf
## 120 @NickRedMachine Ive only ever been taught the carry the 1 method so I auto assumed common core cuz it looks like taking unnecessary extra steps to reach the same conclusion.
## 121 @uscaninc I’m in $HCMC with 4,000,000 shares, but this appears to be some very special kind of common core mathematics.
## 122 @AStopcommoncore @ChairmanWahl @BabsMoore10 @RepBarryMoore @MarkMTuggle @GovernorKayIvey You want to blame middle school suicides on Common Core? That's absurd. Common Core is the biggest mountain out of a molehill in the last ten years. Common Core is a set of standards, period. If you can't measure up, don't blame the yardstick.
## 123 To all those teachers teaching common core, Marxism, woke garbage, social justice hate America to our kids. You are not celebrated, I don’t care about the excuse, like our founders stand up & say no more. You have the most powerful union in the USA & you teach this garbage.
## 124 @ounceofprevent1 @ErikWemple @politico @benshapiro The majority that surround us are not "ordinary" people; the are victims of common-core wokeness propaganda.
## 125 @RyanAFournier Common core demonRat math
## 126 Serving students with autism has become a greater challenge with increased Common Core emphasis on language and communication skills. Check out this webinar for great research on the subject. https://t.co/nohUJGTphR
## 127 i play jumpstart, but math's hard\nand i can't stand learning anything\n i like when grown-ups call me smart\nbut i'm dumb, so i won't add sums up anymore\nand i barely even care that i'm forgetting all the common core
## 128 @johhnyonenote @ProdigalHoosier @BetterIRL2021 @debbshock I’ve had a chance to review the video and the nutritional comparison between one serving and the entire contents. I think the problem is they used that new f’ing Common Core math. It’s ridiculous and makes no sense. https://t.co/jHJ0zFV3lR
## 129 @RoryCapital Send this ass hat to GITMO take all his land and his money including family finances and give it to the American people. He’s already damaged one generation via common core. How many kids of been down in via his vaccine obsession?
## 130 @slappinglibs @RichardGrenell @TimRunsHisMouth @BrianSimsPA I blame common core and participation trophies...
## 131 @Nel65Bob @DaveFine4 @laurenboebert 139 million registered voters, Trump legitimately got 74 million and Buyden got 81 million voters. I know you learned common core math so show me your work. Remember, children, prisoners, dead people and double votes don't count.
## 132 @Thorman_Lungie Are schools not teaching about the civil rights movement anymore? Pretty sure that’s common core my dude.
## 133 @David_Iroquois Lmao you ignored everything else. Seriously dems built prisons based on the test scores of young black children. Introduced common core, crippling black literacy rates. And control over education is literally a chritrria for mass control. And calling liberals intellectual lmao.
## 134 @snobishdesign @CodeMonkeyZ You named three things but then said "neither". Did you attend 2nd grade? Maybe you believe in common core math? You cannot insinuate something is incorrect, be unable to use proper terminology and expect people to care about what you say. What a 🔧
## 135 @SwipeWright @DonaldJTrumpJr This is the same ideology that said every athlete gets a participation trophy. there are no winners and losers. and we need common core math. Every time a black kid gets a grade lesser than a white kid, we have to hold white kid back.
## 136 @yonszenshpensz @yourewrongabout @Remember_Sarah On the third hand, the moment I show a student another approach that makes more sense to them, common core, new, traditional, euro, whatever, AND THEY GET IT, it’s all that matters. Really, I’m just here bc math teaching has got to have a @yourewrongabout episode in it.
## 137 Russia sends 50,000 additional troops to Ukrainian border, US sends 500 additional troops to Germany🤔 Do they use common core math?🤨 https://t.co/qP59A3Msan
## 138 @RadioFreeTom Well done piece. I think the Brezhnev analogy is much more on point than any to Stalin. Mediocrity is the common core. But, re Stalin, I won't hold my breath waiting for any party leader to channel Nikita Sergeyevitch and denounce him (in secret or otherwise.)
## 139 @ChrisCillizza Common core is the devil
## 140 it must be that Common Core "New Math" stuff because I have never heard of this.
## 141 @Ayz @GreyTonka i define "AI" as the artificial intelligence that was instilled in us through our "common core" education.\n\nthe most perfect procedure followers are put on the societal pedestal for a reason...\n\nall it takes after that is dogma and social shame to keep all of the spells alive...
## 142 @jnewman1215 I think it’s that common core math.
## 143 @CaitlinPacific first it was differentiated instruction and diversity, and now doing a complete 180. oh & common core was a failure, so maybe it's the administrators that need to do a lessons learned.
## 144 @DaveGragg Sounds like Common Core math to me ...
## 145 @RumbaRebel13 @disclosetv That maybe that’s plan. Then the federal government will take over. Like they did to education. Now we have common core and CRT.
## 146 Um also I am still SO pissed about common core?!?? Tf?
## 147 @SDunaway6 @DonaldJTrumpJr Killed more than Hitler???\nJesus is that common core math you're using? Explains the stupidity of your answer!
## 148 Those numbers are really common more common than the common core math numbers!! https://t.co/5nmcxUQXVb
## 149 @SusanStJames3_ That's really 6.3\n\nCommon core screwup
## 150 @ThePerezHilton In 19 days, if you hear tires squealing & smell burning rubber, it’s just me retiring after 37 years teaching. Am DONE w/testing & common core crap. There’s not enough clean words or characters available to hear my thoughts on this...😳🤪
## 151 @Justin11914590 @helloamysnow I wonder what they will be teaching in the schools? Isn't going to be Common Core 2.0?
## 152 !⚡strong⚡This book is your comprehensive workbook for 6th Grade Common Core Math.❤strong⚡ By practicing and mastering this entire workbook, your child will become very familiar and comfortable with the state math exam and common core standards. This 6th Grade Common Core
## 153 @catturd2 Is that with common core math? $1400 to pay bills for months? Must be that new math
## 154 Graphic Revolve: Common Core Editions Ser.: The Three Musketeers by L. R… #ebay #book #books https://t.co/XsBsfKWEEp
## 155 @Cosbybill6 @Jeff75998275 @MinarchistR @dwightlwolfe @laurenboebert Really??? Do you live under a rock? Common core is one example - a curriculum required in every state. Utilities and farmers - Obama put so many ridiculous regulations with severe fines for noncompliance. It’s increased the cost of food and basic utilities for everyone.
## 156 i like when people think i'm smart \nbut i'm dumb, so i won't add sums up anymore \nand i barely even care that i'm forgetting all the common core
## 157 @sttngduck Common core English.
## 158 @elleprovocateur @Philly852 Alice Linahan @Woodyboy2020 clued me in on the #lifelonglearning scam and connected me to Alison. \n\nThis book by Wharton + Hesburgh rec’d criticism for the graphic back in the early 70’s bc looks like 😈😈😈 in the mirror \n\nhttps://t.co/H1fLdQYAmS https://t.co/UAW5I1IA6m
## 159 @PoorJohnBrewing That IS the Common Core way! Read its requirements to verify.
## 160 @Giannis__SZN @PFF Thanks for making sense of common core math! Haha
## 161 i like when people think i'm smart \nbut i'm dumb, so i won't add sums up anymore \nand i barely even care that i'm forgetting all the common core
## 162 Ever since the Democrats implemented Common Core Socialist Indoctrination into our Public Schools. https://t.co/btbljE81Ma
## 163 Weird and Interesting Facts | Most Satisfying Video | University of YouTube\nhttps://t.co/i2uK0AkFq8 #CCChat \n#CCSS \n#CommonCore #EdPolicy #EdReform\n #ESSA
## 164 @JimColeBACK @TimRunsHisMouth @IlhanMN Apparently it's all common core now 😂
## 165 Bisnode, a driver of thought leadership, was once 280 companies before all brands came under one umbrella back in 2014. The company worked with Niteco to build a global solution that enabled it to launch 18 different websites, with a common core but local variations. https://t.co/yFskhit5UA
## 166 @TweetyMctwat All the mofos who try to teach and change curriculum without having taught a day in their lives, deserve some teeth knocked out. Common core is an example of that. Off topic but that crap pisses me off to no end.
## 167 Are you ready to beat the #summerslide?\n\nWith more 🌞 comes more time to catch up and guarantee your student’s #success next year!\n\nCall now to schedule a #freetrial!\n\n(619) 946-5686\nMathnasiumofchulavista@gmail.com\nhttps://t.co/OvakI9VFMC\n\n#chulavista #bonita #commoncore #summer https://t.co/WL7wbTtxq0
## 168 @Bowlingpriderj @nikoCSFB @HawleyMO Do the math and science how many registered American voters vs the total numbers of people your media brain washed u to believing voted. The math don’t add up get off the common core & trap music pick up the constitution and learn it.
## 169 @steveInThrPDX @IdkImean @MalloryGates14 @MikeMooreDO No, you're wrong. Most people who complain about common core are wrong and just don't take the time to understand it.\n\nThe question is fine. Jack forgot to move back one for the ten's place. He moves back 3 hundreds and 6 ones, but zero tens.\n\nConcepts should be understood.
## 170 @ToddHagopian I am seeing a lot of ''common core'' math in the responses.
## 171 Meanwhile, I remain agog at this summary of Common Core. How they got here... I have not a clue.\n\n"A few reforms of note have been attempted to improve America’s civic educational system... Common Core appeared to be a promising way..." https://t.co/J0e9Karv97
## 172 @CaWomenforTrum1 Well in their defense \nXy+xx= xxyyx to the second power which leads to xxxy zzzy and foremost Nshdy=hnn1 and its really that simple. Common core\n\nI gots hairy legs
## 173 Obama s illegal executive order on immigration protect the Second Amendment end Common Core rebuild the country needs.
## 174 @EstateRanger It is so much worse than common core, I studied accounting in college, & didnt learn shit. Got to the CPA and atleast 1/2 of the material was new. The problem is the professors really don’t understand the material - i bet most professors have horrible personal finance hygiene
## 175 @LSDinmycoffee OMG, the comments section here.\n\nThere’s some acid trip Common Core math going on.
## 176 If You Liked Common Core, You're Going To Love Joe Biden's Civics https://t.co/5zapL5TnaH
## 177 @wittenp @Jim_Jordan Truth! The problem with the feebleminded delusional sub logical Marxist dem terrorist idiots is the word has nothing to do with gender and has everything to do with with the end of a prayer or a statement to express assent or approval. They are a product of common core stupidity!
## 178 25, Best Memes About Common Core Math, Common Core Math Memes https://t.co/6LKIOKrN4T
## 179 @DanielBolnick I had trouble figuring out one of those common core type questions from the first-grader's homework
## 180 @CNNPolitics For those struggling with simple math try the traditional way rather than Common Core Math. https://t.co/mQv0awZ3FQ
## 181 @rottsbot @OldSchoolCoug @rweingarten Its actually 270%. I learned that in common core.
## 182 @GKCdaily @peterschweizer UK/German Pearson Press has a monopoly on Academic textbooks around the world. \n\nPearson Press monopoly are behind Common Core and government corruption.\nhttps://t.co/fis3091Nnd\n\nhttps://t.co/ZwOM9DzF7D\n\nEverybody Hates Pearson\nhttps://t.co/dFqI9lbFSu
## 183 @Dictionarycom Come on. SUPPOSABLY and IRREGARDLESS do not belong in the dictionary. What will be next? I'm losing faith in the educational system. My middle school grandchildren do not know how to write in cursive. Also that Common Core Math. It makes no sense.\nLet's do five steps instead of 1
## 184 @CoreResponse @moonfrye Perhaps its @usedgov-Common Core-math thing "400 lights [symbolically-reps] 400,000 people" not calculated in differential equations; or was it meant some other means? asking for a friend @POTUS @POTUS45
## 185 Cookie Monster would be shamed into stopping eating cookies and stuck with veggies and soy; Elmo would be forced to change his gender; The Count would have to count with Common Core; and Garbage Monster would also get no financial help - it's for illegals from Land of MakeBelieve
## 186 @Lisak52 Common core results from a govt indoctrination center...here's math... https://t.co/iPCRMVi8vx
## 187 @RyanAFournier Common core math
## 188 @GinaSlyer Common core math...
## 189 @Breaking911 $1400 to pay bills through mid-July? 5 months of bills? That math only works if they have no bills to begin with.\n\nEither that or its common core math.\n\nWho makes this stuff up?
## 190 How did we get to the Common Core and mandated punitive testing for all pub school kids? How did standardization, competition & data worship—a totally UNjust model– become our go-to idea about what good schools look like? https://t.co/IgtBYm77hL
## 191 @Johnny_Nomics @mtgreenee Better: Prove creepy Grandma Joe got 81 million votes, which with Trumps 74 million adds up to over 13 million more than the total number of registered voters..\nEven your crazy common core math won’t work for that one...\n🤪🤡🤔 and we will wait...
## 192 Common Core was an education disaster. https://t.co/ZyJqkNiZwd
## 193 @Sylvia58993342 @osullivanauthor It's toilet paper math and common core mixed
## 194 @Harry1T6 @RealSaavedra Is this common core math?
## 195 @TpT_Official Ever wanted to show your students how to type their numbers (1-20)? Originally designed for kindergartners, this lesson can be adapted for any grade level.\n\n#commoncore #technology #numbers #googleslides #kindergarten\n\nhttps://t.co/Xa7148ATAZ
## 196 These are common core values shared by many. List three of your core values then learn more at https://t.co/TPD1ltPhqP https://t.co/TGYEx6xDRB
## 197 @toniboyy @AGEllison Reagan found U.S. ed failing in 1970s. Feds are deliberately inept by LAW! Why blame LISTS, such as standards (Common Core) while ignoring the contribution of teachers & textbooks for success??
## 198 @BDolanSFR One thing Common Core standards got right was not teaching cursive. \n\nA good argument for cursive I've seen is that it's impossible to read primary historic sources otherwise, which precludes new critique, but that's a time tradeoff w/other subjects that are more widely relevant.
## 199 This is a page on the 2nd Amendment from a workbook from the California COMMON CORE curriculum on the Bill of Rights. #2ndAmendment https://t.co/gWKeakdUlv
## 200 @i_love_hot_momz i cant with you- probably music unless it’s common core classes in that case english
## 201 @TradBritGroup Nicolai Sennels, 2011: ‘the multicultural society will always fail. For a society to succeed, its citizens must be able to unite around common core values. The most fundamental values are national identity and a wish for fellowship with one’s countrymen.’\n\nhttps://t.co/8dPxzJnfgN
## 202 @rweingarten Common Core math.
## 203 @jamft @RyanM_Shuswap We realize it’s simply semantics however we are allergic to promoting the “common core” phraseology in 2020/2021, preferring common sense.
## 204 @murray_nyc @realDonaldTrump 80 million\nYea you're right\nJoe got dead people\nInvisible votes out of thin air\nSwitched votes\n......common core election\n1 vote for trump meant 2.5 for Biden\nOutrageous!!!!!
## 205 @FierceOctogena1 Very funny! Maybe it's the new common core math?
## 206 @jnewman1215 Common core math...
## 207 it started with karens wanting more disposable crap, so they sent their kids to be indoctrinated by evil wolves, beginning with #billgatesisnotadoctor common core, then placed the kids’ participation trophies on the shelves & worshipped them🏆\n\n#liberalismisamentaldisorder https://t.co/MvCkfSwavk
## 208 @JimFannonShow Common Core Math.\n✌🏻🇺🇸
## 209 I didn’t think teachers were stupid🤔 these are the people teaching our kids? 😂 this is worse than common core math.. if math is racist then science is racist because science is based on numbers. I guess “their” math and science is racist🤷🏻♀️🤣 https://t.co/OYqXIab5Zm
## 210 Done Deal for these junkie Common Core antics! https://t.co/tXIATyFqgB
## 211 * How is this related to other debates over what’s taught in the classroom amid K-12 culture wars?\n\nI'm also partial to this line: "A good parallel here is how popular ideas of the common core learning standards grew to encompass far more than what those standards said on paper."
## 212 EBOOK Download Common Core English Workbook: Grade 4 English -> https://t.co/ykbiMTJO2h
## 213 @OrwellNGoode Math was a lot less racist before bill gates f@cked with it!https://t.co/ulxv0wJZyH
## 214 Wait. Is this common core? Can't be. This was super easy to follow, and I'm super fucked up. I am terrible at percentages, and this made so much sense. https://t.co/PttKnnJJqf
## 215 Mobi Download Free Journeys: Common Core Reader's Notebook Consumable Volume 1 Grade 3 >> https://t.co/GQ5L2cxtAI
## 216 @SusanStJames3_ Common core math!
## 217 @MitchMontgomery @Rasmussen_Poll Common core math on display
## 218 @315Rip @RepAdamSchiff 7 follower troll with threats...Go tf home pig..Is that your common core math? 81,000,000 voted for Biden ya fucking moron https://t.co/o6NE4YWfOD
## 219 My neurodivergent ass was in special ed classes throughout middle school and then highschool I was taking all honors classes. Can I just say FU to the school system and government for common core. If you ever think you're stupid...well you're not❤️ intelligence is a concept https://t.co/GgK9DhuNxj
## 220 @cmclymer @KosherSoul Get to work madam education secretary, enough of this dribble. I need more common core standards...
## 221 DEFCON 5 [RED SWAN] Normal hashtag usage: Sean Penn adopting Common Core by Q4.
## 222 @qballhst @JonahDispatch To be fair it's not like teaching needs to be a highly skilled or trained job either. They just "teach" what someone else tells them to... ever heard of Common Core?
## 223 Undercover Common Core Vid: Exec Says "I hate kids...it's all about the ... https://t.co/SevtUeZvZq via @YouTube
## 224 Seems the American citizens That actually know math not that common core bs are Starting to get Tired off the less oxygenated urban population thinking their ideas are the way of the future
## 225 Common Core math they’re passing off in schools. That’s why it doesn’t add up https://t.co/Qbk6rxRfrZ
## 226 @JimRenacci Interestingly, I myself just made the Common Core connection a few days ago. The liberal revamp of Ed System has been years in the works. Blind trust for our teachers/educators/legislatures NOMORE!
## 227 @BradyDarnell @nickiclyne I agree with this one. My son was on the cusp when they started common core math. It didn't jibe with how his brain, and he never got it. They would not accept that some kids prefer to walk a straight line and not a maze.
## 228 @FlashToso @TeddyRepublican @charliekirk11 🌀 See you must be living on another side of the world OR SOMETHING where the water goes counter-clockwise down the drain bc YOU KEEP GETTING DIS SHIT BACKWARDS\n\n👻 Though it could just be me..\nhttps://t.co/5xgQNeYZJe
## 229 Ugly overpriced ebonics common core center incoming. https://t.co/XMd58gQavW
## 230 Mississippi didn't get rid of common core. They changed the name and copy/pasted it onto MDE letterhead. The same with Barack's Ed Tech. https://t.co/NyzzlV0erz
## 231 @Seekerotruth Even if it hurts the people around him? He opposes the Common Core State Standards Initiative. Which should be a must.
## 232 @CopiahDawg @sixpackspeak @clarionledger Common core
## 233 Ever want to show students how to identify the months of the year? Originally designed for kindergartners, this lesson can be adapted for any grade level.\n\n#commoncore #technology #calendar #googleslides #kindergarten\n\nhttps://t.co/bBWcfwAp81
## 234 @daniellstevens @DrTomFrieden Common core math????
## 235 @Kronykal Must have been a Common Core variant...
## 236 @ShreyaJainNYC But…but…but…that’s not part of common core?!?!?!?!?! How would we ever survive with such a USEFUL skill?!?!?!?! Ooooooh the horror!! — please shout this from the rooftops, this is so spot on
## 237 @conspiraboomers Common Core math is out of control
## 238 Commies erase history and truth. The sheep must be dumbed down (common core) to believe everything big brother(MSM) requires. Then the purge. https://t.co/uyPkY9Kjts
## 239 @HankyPa80131152 @ppglane Hes is such a high position w our own gov, w DARPA, w our military in general, whose gonna arrest him? Did you know common core was Gates plan that our educational system followed? I just found out recently. How was that allowed to happen?
## 240 @tarashdev In another day and time, I’d whole heartedly agree. 🙏🏾 But these last 4 years have shown a great decay in common decency, and corrupted power has drained the faith in common core principles of honesty, fidelity, compassion, human dignity, etc. Public trust must be restored. 🙏🏾
## 241 @BeauHawkEd @BreznahanR @pat_pgsims @lachlan @StefWKight Common core doesn’t teach students what they should be learning. It inhibits students from learning at a young age and it can make it difficult to catch up when they get to middle school and high school. Critical thinking is taught when students are actually able to learn.
## 242 @laurenboebert I agree, the DOE needs to go, ever since the federal government got involved in education it’s gotten worse. I know far to many teachers that hate common core and federal regs. They spend to much time teaching to pass tests and not teaching to give knowledge.
## 243 If you want to see the future of education, click the student project links in my Psychology Today interview. SAT? AP? Nah. This is what real rigor looks like. https://t.co/OvJK3zMbdX #edchat #edreform #sdt #interests #edleaders
## 244 @ASimplePatriot @Gregory52594205 Unless it’s common core for leftists. Then the answer is “purple”.
## 245 @steamtug7 It's not even that. Prior to common core a lot of teachers had gotten away from it, or only used it for morning work in elementary school. With students learning to do everything on computers, they would never use it.
## 246 @ConceptualJames Using common core--\nIf you have II and get another II you then have IIII. Just put them all together.\nAmirite?
## 247 @FlashToso @JoeBiden All common core does is teach kids how to fill in a bubble in a standardized test, nothing more. A part of me is glad schools do shut down, we can deprogram our kids and teach them the way it should be taught
## 248 @pvtjokerus Common core math generation
## 249 @GuidoFawkes1976 Must be the new Commie Common Core Math...never could get my head around it.
## 250 @brianjoralvarez Really? Is this common core math?
## 251 @ByeRobPortman @ShinSooTones \n#Columbine was 1999.\nThe common core is Childhood trauma and the anger driving suicide . \nSomeone to blame..\nWhen people man shame for seeing a therapist. \nBullets are their tears.\n#ToxicMasculinity \n #CommonSense https://t.co/rWJfWG88kQ
## 252 @1onlybillyshea1 @maxsec @cxpage @doc_lamb @doctorbuttons @FlatSlugbrains @CoderPW @TechnoGAUNTLET @badibulgator @UnCastellsMes @tarawasjesus @Mctoon27 @ReadECAM @DJignyte @NelsonMKerr @DonnieJ___ @SometimesRatio1 @keigh_see @SimonTe53732612 @Captainswoop1 @GabeMaiberger @VinceGottalotta @2Bigfootrules @RunningHippo @SwingTheHammer2 @FEtranslation @SurveyorMaine @Dwyertd @LaikaAndYuri @mikeonthebayou @Huttvalleycraz1 @oudeicrat @ScottoftheBruce @SimonGr41594862 @SpearOfNeptune @HuffordShawn @tgphysics @goofy_1_goofy @LogicalReterg @RobParr2020 @IMechE I graduated before Common Core changed how kids add and subtract large numbers in their head. This new methodology doesn't invalidate earlier ways of arriving at the same solution. Your "point" is only a pertinent one to you.
## 253 @NasserAlarifi0 @JohnGor77222373 @SexCounseling @LATAMforTRUMP Common Core math
## 254 @melisalw Apparently proper handwriting is not a part of the common core.
## 255 @OleBrotherNoah @SpeakerPelosi I don’t recognize the math y’all are using. It is nothing like they are teaching in schools. Common Core right??
## 256 DISTRIBUTIVE PROPERTY OF MULTIPLICATION //3RD GRADE COMMON CORE MATH: Hey Friends, Todays video is breaking down the Distributive Property of Multiplication! We will be breaking down 2 multiplication equations by using the ... https://t.co/fS5aqRIGDa https://t.co/WWMb5gntfL
## 257 @__Mansfield__ @zoe00272815 @RealMattCouch @jsolomonReports Common core.
## 258 @FlashToso @crvnberrie Common core is the worst at showing thinking in math. And for majors like finance and accounting, you don't need "critical thinking", you just need to learn how to plug in numbers, which math for the most part is supposed to be. Critical thinking should only be in English.
## 259 @KatyaSedgwick Lol common core statistics
## 260 That Common Core math be whooping y’all asses https://t.co/tXDtT1M1Ql https://t.co/GgXmY0OxPr
## 261 Common core bullshit https://t.co/dVpewtukTe
## 262 Kimberly we love you -it’s not their fault they can’t understand numbers as they’ve been victim of the common core extreme math agenda!!! https://t.co/3fD4lq6pfF
## 263 @megan48528051 That must be common core English
## 264 Must be that Common Core public school math. \n80M Biden votes + 70M Trump votes = 133M registered voters. \nMakes perfect sense to me, don't send me to The Camp Uncle Xo. https://t.co/BLAyGHI9HV
## 265 @MajorMontag the reality is that common core creates communists...
## 266 It's common core math. https://t.co/f3uoTJHo2S
## 267 @KinglyRed Every gacha system does. If nobody told me KOS-MOS is in Xenoblade 2 I bet I would have gotten her in my first common core.
## 268 Divide and Conquer: An Adult Course in Common Core Division. Step-by-step guide for understanding and applying Common Core division concepts. This course contains videos teaching new ways to divide that students are learning in school today.\nhttps://t.co/W3YWh4nD4F\n@physorg_com
## 269 @CariKelemen Doing common core math,that makes her worth $11.1 million dollars
## 270 @Cybershell I believe it's called the common core method nowadays. Both kids and parents have a hard time with it spending hours just doing homework and trying to figure it out!
## 271 Insane how common core requirements work. Wanna have a major with the department of health and behavioral science? Take geology because you have to! Wanna help people with psychological issues? STUDY ROCKS FOR A SEMESTER!!
## 272 On the real tho, I would really love to know whom at @LASchools decided to use this common core math curriculum which is torture!!! Having to learn this method as a parent in order to help our kids is insane! No wonder kids are struggling in math smh
## 273 @red_baiting Must be common core math.
## 274 The #AirForce has Platform One. The Army is creating cArmy. And now Special Operations Command is jumping on the #DevSecOps bandwagon with its own version called Agile Dagger. Check out the article to learn more. https://t.co/vozrSlUk4x
## 275 @BedBathBeyond Is this #CommonCore math? https://t.co/0d9euPym41
## 276 Common core math https://t.co/gyJa74JyEv
## 277 @brithume Common core graduate
## 278 @RyanAFournier There’s that darn common core fuzzy math that they are so fond of rearing it’s head again. 2+2=5. $1,400 is really $2,000 because $600 is the penalty you must suffer to pay to get that.
## 279 Common Core Math..🤣🤣 https://t.co/WbnmN1Znuo
## 280 @JrzGrly @kirstiealley Is this common core math? 🤔
## 281 @USSLiberty14 @BobTheBot2 @LawyersTribune @ElijahSchaffer Your generation has been dumbed down .. common core style...and on purpose! Obviously!
## 282 @CitizenFreePres you have to have an ID to get a vaccine, stands to reason that you have to get a vaccine to get a pass therefore by pre common core math, I figure you need an ID in Chicago to have your vaccine pass.
## 283 @StanTradingMan common core math —- \n\nBuy high, sell low. 😂😂
## 284 @sniperliu @ColumbiaBugle @Cernovich @jimmy_dore Common core
## 285 This was my mood during the Common Core debate Indiana had a few years ago. https://t.co/E2M8eLv0Vq
## 286 Over a decade after the release of the Common Core State Standards, there is no convincing evidence that they had a significant, positive impact on student achievement, @tomloveless99 writes. https://t.co/xABALqyesH
## 287 @BrockLennon Kate Kelly. Congratulations. You may be the dumbest ass hat on Twitter. Ever. Is that common core history? Or woke history? Either way you are a complete dipshit.
## 288 @chechevache @LordCys @DGComedy The eagerness to privatize standards is a big contributor to the problem. Common core is a joke but not because the standards are inadequate but because it’s left up to states to choose how they’re implemented.
## 289 @belledejour_uk @EricDJuly Common core history https://t.co/sQaJd26s9m
## 290 @divinedre11 8+7=15.\n 4+2=6. Carry the 1 over =7 \n75. \n\nI was wondering why this was trending since it's just simple math but then I remember common core is a thing.
## 291 @NotNiceDavid @wil_da_beast630 Two words my friend: Common Core \n\nI'm pretty sure it's the reason why nobody understands large number math/statistics. Once you go over a billion people go braindead. Congress' #1 tool against the public.\n\nCan't wait until the roll out common core social justice classes. 😂
## 292 Let’s recap:\n\n• He laid off tens of thousands\n• He still hasn’t sent out the stimulus balance\n• He reneged on his promise to wipe out student loan debt\n\nCOMMON CORE: Making everyone dependent on the Dems for $$ and it’s all about the $$.\n\nThey’ll say anything to get your vote.
## 293 @Scott_821 @gin_soakedqueen @mtgreenee Oops I used common core math - it’s 9 years bc she’s more hypocritical under the Joe regime - actually her M.O. began in Balt with her corrupt family \nBye 🤖
## 294 @BearingtonTrade Bro - no one can do common math anymore now that they teach common-core math. But they all know what's best LOL
## 295 @bridgetphoto @LaurenGruel @CNUSD Common core is a set of standards. It is not a curriculum. There is nothing wrong with the common core concept. It allows for a common set of standards students learn across the country to ensure kids in this country are accessing similar concepts; that you can go to any state
## 296 @LauraWhitt32 Common core at work.
## 297 Cultivate Reading Carson-Dellosa Kelley Wingate Series Common Core Edition Grammar Workbook, Grades 3 - 4 (Ages 8 - 10) Cause <~′Classic′ - a book which people praise and don't read.
## 298 Divide and Conquer: An Adult Course in Common Core Division. Step-by-step guide for understanding and applying Common Core division concepts. This course contains videos teaching new ways to divide that students are learning in school today.\nhttps://t.co/XZg9Ro8lID\n@matt_levine
## 299 @nicolealicerose @bradbatt @GOP Yep. Common core math is actually quite a good strategy to do math. 🤦🏻♀️
## 300 ➥ https://t.co/eOhWe9zDsC\n✧EBOOK Download\nPeter Pan (Graphic Revolve: Common Core Editions) by J.M. Barrie & Fern Cano ✧\n∞ status ready ∞
## 301 This week's Focus Maui Nui look's at Hawaii's only school offering a curriculum of Hawaiian lanaguage, culture AND a common core grading system created entirely by the kumu (teacher) https://t.co/AoQ7E1fwWc
## 302 @Kim_Gregoire woke media looking for new levels of wokeness. correct outcome but disagree with your thought process getting to the same end. just like common core math. painful.
## 303 @MoormanPercy @Sagittarius3020 No, I was talking about my son’s teacher. Using this common core educational plan to indoctrinate the children.
## 304 Oh she created the common core I fear https://t.co/ovX07FKhVQ
## 305 @ANTHONYBLOGAN These guys are gonna gurantee that our future is fffff’dup...common core, cancel culture, plexi prisons, teacher’s unions, critical race theory/diversity training, 1619 and gender fluidity...Did I forget something?
## 306 Can we cancel “common core” teaching? Clearly it wasn’t working... idk why the US felt that lowering the standards and using jargon was effective.
## 307 Hey. Hey, moms crying into your wine because you don’t really understand the common core math you tried to explain to your kid after their zoom class today. At least you didn’t take them to overthrow the government. You’re doing great. 💚 https://t.co/TcBm3d7VRb
## 308 @DTrain89009 A Common Core education?
## 309 @itsFilmatic “Whales.”\n\nAmerican Common Core education strikes again.🤦🏼♀️
## 310 @wolfjon4 National Educ. Standards and Common Core are essential to protect truth, historical, scientific accuracy and critical thinking skills. Teachers and teacher’s unions have fought them because of accountability. An educated citizen is the purpose of public education in a democracy
## 311 The American resue plan IS the green new deal which says the Industrialized nations must be deindustrialized. The reason for the incremental dumbing down in academia and schooling is purposeful and is obvious from common core to sjw's in schools.
## 312 @AdrianAssman #CommonCore I bet.
## 313 @tedlieu How do you do it with Common Core math? #CommonCore
## 314 @mrbevs @megynkelly You must be using common core math....
## 315 Common core would have prevented this https://t.co/wpiErxNY9q
## 316 Check out our online reading and writing summer tutoring programs where we teach students to read complex academic English. #commoncore #english https://t.co/k10Yweaf2l https://t.co/Y173GYyaZz
## 317 @Rome_Fell Common core math?
## 318 @TheGatorGamer They're using that Common Core math.
## 319 @LoveHerMo @jjauthor Common core math
## 320 @adamslaw2002 @GovBillLee TN is not a common core state abs hasn’t been for 4-5 years. The problem isn’t dumbing down our curriculum, it’s quite the oppposite. We are teaching things that are not developmentally appropriate and pushing to much too soon. They actually need to scale back some things.
## 321 Common Core Math: Does the Biden FRAUDministration make sense NOW? https://t.co/bZxDvi900v
## 322 agreed to even before they were written down.\nThe promoters of Common Core always insisted it was *only standards* and not about curriculum, materials, or type of instruction. But of course standards drive curriculum and instruction, so it was a lie from the outset.\nAs this
## 323 like ive honestly considered getting my ged, esp bc idk if arizona recognizes homeschooled highschool grads. but also im in arizona and DESPISE common core
## 324 During his one term as Superintendent of Education, Zais was "criticized for refusing to accept federal education dollars and rejecting Common Core curriculum proposals"\n\nhttps://t.co/sBNqwt962q
## 325 @catturd2 85 masks + 4 shots = 0 covid protection (common core covid math)
## 326 Download Kindle 6th Grade Common Core Math: Daily Practice Workbook - Part I: Multiple Choice | 1000+ Practice Questions and Video Explanations | Argo Brothers >> https://t.co/zpWBhK64WY
## 327 @oldguy_steve No child left behind & common core are progressive curriculums have twisted every aspect of education therefore our children have been dumbed down & taught falsities about history, math & common sense. They are being taught for tests & mined for future socialist voters & workers https://t.co/p4biojJqY0
## 328 I’m excited to see the common core kids grow up and be rocket scientist. https://t.co/kpC4wnBD9W
## 329 @mpetrillo59 @SheriAWilkinson Sadly, cursive got dropped yrs ago. Common Core hit schools w/ so many curriculum reqs, it became 20 lbs of spuds in a 5 lb sack! Teacher plans must document every sec of each day & subject, leading to absurdities like having to call a single 5 min. bathroom break ‘health’!
## 330 @laurenthehough As a college professor I was often forced by way of the common core to read his overbearing nonsense, and that of others, for my job.
## 331 Instead of all the common core shit, can we just make sure that the next generation understands that 1:1000 >> 1:500,000 > 1:1,100,000? #MakeMathGreatAgain https://t.co/ZdMUSHCLb1
## 332 @votewarren Common core math
## 333 In our group Snap, there’s a consistent complaint that the professor “didn’t prepare us for the test.” The professor adequately taught the concepts and told us what concepts would be on test. I’m 35 so I missed Common Core, new math, all of that.
## 334 @ProudGayPatriot Just another mediocre idiot....the product of common core....🤪
## 335 Don't forget about this #free #commoncore aligned #chemistry assignment #STEMED #HOMESCHOOLSTEM \n\nhttps://t.co/21tvPA86Ib
## 336 The first is a heavy-hitter: freshman Madison Harvey explores the downfalls of Common Core, and how students may feel stuck in the trenches of "General Education" --> https://t.co/ou8x57KV3C
## 337 @BarkTwain @jaymills @LouSchoolBeat @kugler_suzanne If we are going to have an equitable education across the commonwealth, the state does have to at least stake out a common core of material that all students should learn. Otherwise, highly inequitable education will continue to exist in Kentucky, as it does today.
## 338 @KAMbot1138 @rani_yachts @Jkburke21Jim @disclosetv Lol Common Core didn't teach them History
## 339 @Ctogto1 @LikeFineWine63 Common core math 🤦🏻♀️sweet mother of God 🤷🏻♀️why just why..
## 340 One should have absolutely NOTHING to do with the other! The teacher's union has become a behemoth and wields too much political power-alliance w/the left. We have teachers who don't give a crap (NOT ALL) and can't be fired & common core curriculum which teaches our kids nothing. https://t.co/wR90FR4fhI
## 341 @thompson_marv @JeffCaswell4 @PastorDScott @GeraldoRivera He was a secret service agent for 12 years. You must have gone to a leftist common core school. Math is essential.
## 342 In other words, it should represent a common core and you're mad at what is already understood as core truth
## 343 I would love to spend a day in a Finnish school! Stop #commoncore https://t.co/D08hFdqpUw #earlyyears
## 344 @SSKedreporter The NWEA at least evaluates students contingent on common core and able to me performed in more than one session. This is horrible, who’s idea was this.
## 345 @robinmcstay @RyanGirdusky Many people who value education don’t want the fed gov to get involved with it.\n\nNearly everything the fed gov touches turns to shit. (think common core)\n\nUnless there is an overwhelming reason why ONLY the fed gov can do something, they should not do it.
## 346 @kromexstylez @realDonaldTrump AW the common core dummies
## 347 @tdotfish @DAkacki That's always how I've done math and it's not something I knew was taught. I would have done so much better with a common core curriculum
## 348 @BrandonStraka Common core math 🤷♀️
## 349 #COVIDIOT #COVID19 \n\nWas your math common core? Or did the lobotomy you are still paying for stunt your algebraic equations? \n\nKnow anybody who got influenza last year????? https://t.co/A5WQwnBwS4
## 350 @T_S_P_O_O_K_Y #CommonCore at work ladies and gents.
## 351 @SjonesiProduce @politickingapp It's common core math.
## 352 @RyanAFournier Common core math
## 353 @AsTh3W0rldBurn5 @thinkmo72216487 @BidenLs @MustelidFan69 Dude it's common core math duh.
## 354 @elakdawalla Is it based on discovery and advances? Or is it more like trying to switch up stuff like "common core" math?
## 355 @WawaIsAPlace @adamental @dcexaminer @CocaCola @pepsi Well incoherent to somebody reading at common core third grade level .. the rest of us understood it perfectly..
## 356 I tutored kids before and during the Common Core math roll out... the mainstream "gifted" kids were always going to grasp the concepts, but the ones who were "struggling" got so much more in their math toolbox with Common Core.
## 357 @jsolomonReports @GwenTThompson1 We are still waiting on Governor Abbott to do away with common core!
## 358 @ConceptualJames Great! Can we grade the kids on their intent instead of their achievements? I’m sure businesses will be happy to distribute salary increases based on intent as well. Common Core is designed to cripple the capable, a truly evil and perverse way to close the “achievement gap”.
## 359 Carrying the 1 takes no time at all what is this common core shit? https://t.co/UiAV3ZeXxa
## 360 Common Core Math or was cheating involved? https://t.co/PpZdfCJ1vd
## 361 Have #Common-Core Standards ruined our children's #handwriting? https://t.co/KGjSwVIJW9
## 362 Why the Common Core standards failed — and what it means for school reform https://t.co/JAobQ4CtBw
## 363 Didn't realize Common Core math had spread so far. https://t.co/QaYyS7HMYn
## 364 @jenben9195 @Xxenology @AnimeKi60453931 @BNobabiess @Taylor510D @sheluvsbandari Ehh common core is a lot more useful in hs/college math
## 365 @SenTedCruz They're too busy using common core math to figure that equation out...
## 366 Read Free Common Core English Workbook: Grade 6 English => https://t.co/w9Xn3oHdEF
## 367 FREE RESOURCE | Print this kindergarten sight words list for reference: https://t.co/2F8NVxaLCt\n\nThe ability to decode words is crucial to reading, but a number of high frequency words are still worth learning whole. \n\nCommon Core: CCSS.ELA-LITERACY.RF.K.3.C\n\n#lockdownlearning https://t.co/vLCC6jRPCg
## 368 @TedJoy71 @kjgillenwater That New Yorker must have just learned that Lincoln was actually a Republican - not the Democrat that Common Core had taught him . . .
## 369 must have been a student of common core...🤣🤣🤣🤣 https://t.co/Oomd3ZNche
## 370 @natebargatze @DanSoder I too am no no longer dating her, and would just like to win a fight. Also 46 trying to teach common core math.
## 371 @CmonMattTHINK The 'new math'& common core math HAD CLEAR & EVIDENT ulterior purposes than recreating the wheel. My 9 y.o. son shared that his teacher told students to not trust their parents to help them w/homework because they will get it wrong. He was brainwashed to not trust our help at all
## 372 @FindingTruths @jimaw63 @Shopdogg2 @boodacat2020 @TomDoubting @LadyOfTheOcean1 @DanaCos @lsuecamp @EmmalineClemen4 @hrt6017 @JohnLepper6 @PhuckSea @4JOC1 @rayl1975 @WillieB1two3 @GQPklepto @Klee34036780 @Birdwatcher291 @ThereseOSulliv2 @r0xie_f0x @SlimJimJohn1 @Flattielover @lostandlovinit @SpRiNgStR3Am @JDW714 @MrPantaloni @chrisg409ubc @laylow88861429 @RoseannaBandan1 @HGoheavily @bacchaus351 @RogerLHaviland @ToccyLa @KattMist @IFDrinkLib @wookietim @Keithjo05883263 @ChiefLizWarren @Gun_YouDown @Leo97894180 @Mongo3804 @JOSIE24055239 @4Cocacoladave @cbowling4512 @MomoaScrunchie @Heather4amazon @psfnyc5 @corinne_locher @ButDidYOUSeeIt @Black_is_back5 Apparently, 1985 is 20 years ago?? Do you do the "new math", or common core?? https://t.co/CvYtmC9Gah
## 373 @GretaGFreedom We try to do the same (ex Common Core) and our education system gets blasted by people in our own country. “What was wrong with the old way” or “when will they ever use that”
## 374 @gult_wala I actually like Bowman a lot, but this is pretty cringe. I know that he opposes Common Core, but this is a terrible reason for opposing it. I thought he opposed it for a good reason.
## 375 Is this common core math? They gave more than they had? How? https://t.co/qQ0rhVczPN
## 376 @paulapoundstone\nCongratulations on this week's Wait Wait triumph! All those push-ups have strengthened your Common Core
## 377 @dr_pete @MendoKC @kaijeffers @AbeFroman @divinedre11 While I agree this is a very natural way to simplify the math, the part I ademently disagree with is that this is the only way being taught. This method worked for me b4 common core was a thing but it doesn't work for everyone. It's whatever makes it easier to grasp the math.
## 378 Project-Based Learning in Elementary Classrooms: Making Mathematics Come Alive, is available for pre-order! It showcases PBL units addressing Common Core State Standards for Mathematics to demonstrate how PBL works and the learning that results. https://t.co/QOv1XdTDOK https://t.co/XuD1UhQx4d
## 379 @LouLou93975708 @JalelahAhmed @laurenboebert Only a leftist taught in a common core school would believe Turnip Brain from Delaware could win after seeing these https://t.co/DF7Z9izX0n
## 380 @StJamesGraphics @AP @shaunking Common core math says 2+2=22. OMG a you people are stupid.
## 381 EBOOK Download 4th Grade Math Workbook: Common Core Math Workbook -> https://t.co/KgqAqc4b1H
## 382 Common Core Math https://t.co/ocAwtuPtBj
## 383 @PolitiBunny Everything is fine. There is no censorship, the govt is here to help you, the Harris Administration will usher in an era of unicorns & rainbows, and everyone will be happy.\nOh, and have you tried common core math? That may be the answer.
## 384 This isn’t common core.....it’s common knowledge https://t.co/69WeymcCUr
## 385 It’s me realizing everyone did math the common core way in their head and I was the weird one that didn’t
## 386 @commentator07 @AZGOP Let me be perfectly clear: The issues we had with AZ Edu had everything to do with what former AZ Gov. Napolitano (D) with her very heavily Dem House/Senate and Az DofEd enacted with policies - specifically as it relates to SpEd, Common Core practices & Bully policies.
## 387 @kostuch Don't even get me started on Common Core
## 388 @OrionPhinery @KPMarie48 That’s right. They’re evil, corrupt globalists. Just like Biden’s handlers. They also brought us Common Core. The goal is the dumbing-down of our kids. That initiative has been very successful.
## 389 Mobi Free Common Core English Workbook: Grade 1 English >> https://t.co/H57xmmYiYz
## 390 @MsMessineo @MoniseLSeward Common Core also has a bunch of decimal stuff by hand. I’m all for understanding how to multiply/divide decimals by powers of 10, but long division with multi-digit decimals? Is that really what we need to be spending time on?
## 391 @paul_emerich @kalmanlafer People have been citing evidence for millenia. That is not what's distinct about the Common Core. What is distinct is that students must cite text-dependent and text-specific evidence. Never share your own thoughts.
## 392 Great guests. Serious, tidy and friendly. Highly recommended guests. Thanks for your trust in booking with us and thanks for leaving the place in such a perfect condition! We hope to see you again! Prentice Hall Literature: Common Core Edition by Unknown.
## 393 @CNBCJulianna Rose Webster @GetMyGist here.\n\nIt was a "negligible" risk with #Zika. Recall it? Shrunken 👶 not alarming enough #Pediatrics?\n\nCommon core #math @AstraZeneca @UniofOxford?\n\nMeta-analysis: PICK data #doctors will believe to make ppl #takethevaccine.\n\nWhy the #Fake #CovidVaccines? https://t.co/p2nFRGRv4l
## 394 DYK? The Common Core of Data (#CCD) is the primary database for information on public #K12 education in the U.S.\n\nAccess our 2019–20 CCD universe data files, web tables, and indicators: https://t.co/lr6CtvAqjF @usedgov https://t.co/0xrVRNRSiy
## 395 @yasnyl016 @JackPenny22 Actually I think that’s common core😂 I just don’t understand it
## 396 @jamie2181 What age are the kids, what is the core curriculum being taught (Common Core in IL doesn’t assess on history knowledge), is this a sub or a legit teacher, are the kids messing w/ him? I taught for 10 yrs, never tried to shame my kids for what they don’t know.
## 397 @FW_Education Does the IEYC fit to allow American curriculum schools to deliver the Common Core Standards? Email sent but fast response needed please.
## 398 Question for the researchers out there:\nDoes anyone measure proficiency and/or growth of the 8 Common Core Standards for Mathematical Practice (SMPs), something that could replace the current tools we have for standardized testing?\n\n/11\n#MTBos #iteachmath @chrisbuttimer
## 399 @jb109cpd @amuse @SheilahBird 150 counts for the 4 people\nEven with common core math that comes out as an average of \n37.5 votes per person.\n\nIs voting 37 times in one election ...voter fraud?\nIf not I get 36 more votes
## 400 my dad is teaching an elementary school zoom class and holy shit common core math is bad
## 401 @TheCourtesan1 @AndyLeeParker1 @USlawreview @MagtathMaggie Thanks to this new Common Core program, Google has designed algorithms to learn about and track our children, for their entire life - beginning in these public schools.\n\nhttps://t.co/FMynWPaIC6
## 402 8 years ago today on the blog: Chaos Theory, Twitter and the Common Core https://t.co/GjAVf5n4dw\n\n"What effect does something like Twitter have on complex, dynamic systems?"\n\n#FischbowlFlashbacks
## 403 @ruffinmuffin_ But the school system doesn’t even acknowledge that there are multiple intelligences!! Common core is so fucked, like it assumes the only way ur smart is if you can use all the methods??? That’s v ableist,,, like I think I’ve got ADHD and abstract math would fly over my head???
## 404 @graceearthur @JackPosobiec @washingtonpost 1200 + 600 =1800\nThis is why common core math is a bad idea folks
## 405 @hardRwonk @ResistScaryBear @spurtmagoo Teachers didn’t “fail” anyone. Common core did. Teachers are expected to hit certain benchmarks at certain times in the year. You can’t slow down to help those behind. If you don’t hit standards, or if your students test badly, you could lose your job.
## 406 @ScottBaio Been around for years, LONG before Common Core.
## 407 @FCEA_KY @Kristin081200 @JerryTMiller @jrajra Maybe stop having such a shitty product and actually do your jobs. Teach useful things instead of piling on useless information that's absolutely useless in the workforce. Common core is the most idiotic thing on this planet.
## 408 @wendyp4545 ALSO SEE: Patriot Act, NSA, TSA, No Child Left Behind\\Common Core, weaponized govt agencies, return of $1T+ deficits...\n\nObverse side of the Uniparty coin🤷♂️
## 409 ⚘ EBOOK Free 2nd Grade Common Core Math: Daily Practice Workbook - Part I: Multiple Choice | 1000+ Practice Questions and Video Explanations | Argo Brothers -> https://t.co/ooxoWqWKQB\n\n⚘
## 410 @thehill Start by getting common core and “equity” out of the classroom. Individuals are uniquely gifted in specific areas. Stop trying to dumb students down to artificially “raise” others up.\n\nTeach personal money management and growing/raising one’s own food. Food is medicine.
## 411 @oldspeaker1 @FoxBusiness The leftist have apparently embedded common core math into our public schools now they want to integrate common core science and they have enlisted twitter and other techs to make sure people who actually know math and science are not allowed to get in the way.
## 412 @shestheman @idiotatlaw @NOTmisternoname @R3linquish3d @KimuraJinketsu @therealhoperose @sweetpotatoebb Yea but guess what, people have no idea about monetary and fiscal policy because it is not taught. We have the most monetary, fiscal, and economical illiteracy in a very long time. Common core math, science, & BS electives aren’t really doing anyone anything it seems...
## 413 DEFCON 3 [MINIMUM VIABLE] Very high Cloud activity: Michael Bay adopting Common Core before Q4.
## 414 What is with locals and unsolicited advice on marriage that has nothing to do with common core values but instead it's always "marry someone like your mom/dad" or "if they have RMXXXX and this car"
## 415 @killminusnine @Reverend_Banjo it's probably some common core thing where they thought kids would be confused having a=area when learning geometry and a=slope when learning algebra
## 416 @thedrugtribe Just some simply common core math like 12x42=? And i dead ass came to and my answer was (^^^) and i asked my mom why i dod that shit and she had no clue. My poor mom had to see me do so much dumb shit lol
## 417 @PatriotJenn1fer Common core math on display w/a side of dementia. \n\nDamn, even Nancy Regan kept Ronnie’s Alzheimer’s hidden better than Dr. Biden’s doing.
## 418 @tweettruth2me At this point, who knows what the real numbers are, does it exist, sure... does it exist in the context that was hysterically communicated, I doubt it... math is hard, numbers are hard, maybe this is a rounding error due to common core
## 419 @RyanAFournier Common core math.
## 420 @haveaconcern That must be common core math
## 421 @SamuelWBell @BridgetEileenRI @matx1800 Moving to DMs for character limits, but happy to share my pro Common Core arguments for anybody interested :P
## 422 @KressePeter @Gotitans333 @valiant_ed @Fischer0508 @Breaking911 When I filed my taxes using Common Core math and getting $26,487 back https://t.co/HHjgkZPHLx
## 423 Read it on PsychToday - Fear of Flying's Common Core https://t.co/jFa2g6FsDx https://t.co/xIFZPJ7Bu6
## 424 @lollyrots what’s common core? i understood the math you drew up. that’s how i think.
## 425 @CassandraRules @MarinaMedvin Let's just review the past 20 years. Neocons started 20 years of war (& still are supporting it). Bailed out silicon valley and the banks with our & our children's money. Lied to constitutes about getting rid of Common Core & Parcc (Pence, Christie, etc.). That's just the tip
## 426 Next stop: CyberSpace! Purchase @Cyberchase Fractions Quest now to launch your students’ fractions and problem-solving skills into another world. Learn more about this research-based, Common Core-aligned math learning game here: https://t.co/GoUOAF9ie2 #FVGames https://t.co/WaBDhyKTGp
## 427 This reminds me of a conversation I had with a room full of college professors about the common core standards, back before they were called the common core standards. 1/ https://t.co/YDbtVhc2mX
## 428 common core 👎🏽👎🏽
## 429 @BreitbartNews just like common core... lets make everyone more stupid
## 430 Are they using common core accounting for that? https://t.co/6K6Macf88F
## 431 @Kazeself @wittywax1 @Amy_L_Partridge @Peter_Grinspoon 🤣 Yeah, still trying to figure that math out. My conclusion? They used that Common Core math shit. 🤣
## 432 Look your kids in the eyes and tell them no for the 100th because you can’t afford even school uniforms for a common core ran school. This whole thing has turned us in to shit parents bc we literally can’t afford basic shit and having to make due. https://t.co/G2No2fLGvk
## 433 @TeamPelosi You borrowed ~$15k from every tax payer and gave us a $1400 check. Is this common core math. Not every American is stupid.
## 434 @RantyAmyCurtis @AspLovePolitics @MadameMiIitant @DLucasDesigns @jacelala He attended common core health classes ....
## 435 @TwoMartinisPls @rasmansa @capetownbrown It’s a similar situation to when people were screaming about Common Core, only this time it’s a bigger group because race is involved & the racists are fired up about it. In both cases, though, none can actually define the thing that has them angry.
## 436 @crowcawer Common Core has serious problems, just not the ones the extreme right thinks it has. We need standards created by teachers, not testing companies and billionaires.
## 437 If You Liked Common Core, You're Going To Love Joe Biden's Civics https://t.co/uzHh0dciz7
## 438 @CBSEveningNews Even though this doesn't affect me directly. The education system and the strings attached to that money is broken today #commoncore. Throwing money at something broken won't fix things. The welfare state is coming. Wonder what the demands will be that come with it. Obey or else?
## 439 @Miss90s When I see common core equations written out though, they look way more complicated.
## 440 @NCl77151047 @ABC Yeah, love that common core math and blm readings.
## 441 ZTE 5G common core validated for Red Hat OpenShift\nhttps://t.co/2h4Tn2nTvk
## 442 @GWillowWilson Common core teaches different Strategies for adding and subtracting such as regrouping (borrowing) and compensation. I would say look at which specific strategy they are using and YouTube that
## 443 @RenaissanceBum @waitisovah Every state has similar common core standards. If anything, in some schools, kids get access to more college level math. I had seen all of this before and my school was in North Philly. If your school wasn’t hitting everything they needed to hit, it’s because y’all were behind.
## 444 @davidrushe @Adrianmilesarch I have no problem of the paper being left leaning and hold a certain political position, but to get the facts so wrong in the first place is infuriating. Like the previous article, to somehow imply architects have the power to change the brief to have a common core is laughable.
## 445 College Daughter [Facetime]: Hey dad can you help me with a question on my Quantum Physics project?\n\nMe [helping youngest with Common Core Math]: OH THANK GOD SOMETHING EASY
## 446 DEFCON 1 [TURN ON] Low Chrome memory usage: Starfleet adopting Common Core in this quarter.
## 447 @adamscrabble Common Core math.
## 448 @jaketapper @afbranco that, and you only know common core math so .....
## 449 @MrAndyNgo @ScooterCasterNY Why Could he not plan far enough ahead to bring a ladder or maybe a long handle roller? This is where common core has failed us...
## 450 @BostonCathSupt @soren_schwab Why the Common Core battle still matters. The standards still structure the SAT, ACT, AP, most (all?) tests used for ESSA.
## 451 @Bakari_Sellers You put a Common Core equation on her head...strong work😂💪🏾
## 452 @TartarusRespawn Common core > common sense
## 453 @StevijoPayne @marketman52 Exactly. No cable, no cells, no internet/social media and a common core belief to do what's right for everyone.
## 454 @miss2virgils @bbyoffgun @lytw8t @Cshenatural @josephlboston @_TheSweetheart Lol. Mkaay. Meanwhile you still don't know what Malcom said about that later on. I'm not even paying this convo any mind. You're not bright. Your grasp of history resembles m I ne as a teen. And your discourse level is💩 \n#nclb #commoncore did their jobs. 🤷🏿♀️ https://t.co/vSwyfQKgtO
## 455 @ArevFTW Oh WANDA-VISION, that makes so much more sense. The whole time I kept asking Deb who Juan is and where the Division comes in. I thought it was because I don’t get common core or something.
## 456 @Robert76907841 @FChecker76 @ejwwest @EDavidAn @Veritatis2021 @steve_niemiec @MorseBart @LochChesney @Climatehope2 @suffect @FeralFox3 @DawnTJ90 @Youcantbeserio6 @morme1 @BJChippindale @ZSRenn @freeman9547 @RHMcK1 @canncr @Bos77 @MhehedZherting @EdBohman @JackFord54 @LesJohnsonHrvat @aDissentient @Convict025 @PorchProgram @BrexitMania @Robin_Hagues @rpini @johnson_wrjohn1 @jwickers @HiFiWhiPhi @BrianTu85458331 @quieroserabuela @timoteo0167 @EcoSenseNow @OLDDYNAMITE @jbonbon91 @DanCady @BointonGiles @KeillerDon @bobathon @Durant1899 @moonrakin @TaroIstok @cosaingalway @wilburcobb8 @stockypig Common core math. 🤦♂️
## 457 @ShadesofBlak @TheBrandiRhodes @RealBrittBaker No such thing as the 'normal' Common Core method!! CC REQUIRES the traditional, efficient method.
## 458 @pogchamp200 @divinedre11 That doesn't make you thick. We all learn differently. As long as you get the right answer, the process doesn't really matter as long as it works for you. Don't give me that common core math crap, or I'll never get anything done. I do it faster in my head.
## 459 @PaulBrakefield @ACTBrigitte Common Core Math.🤣
## 460 @AdamLefkoe This is what happens when kids learn math through common core 🤦🏻♂️
## 461 Thanks to Common Core Terri Collins, Speaker McCutcheon and Alabama Legislature for insuring HB440 dies this Session. #alpolitics #RollTide Guess ignoring parents call to repeal Common Core is a Must and being the Bottom in Education is unimportant. #alfa #AlabamaPower #BCA https://t.co/oA5g4Y7QjM
## 462 @thereadingzone Common core standards had already unrealistically skewed what was expected of kids (not developmentally based) students are not suffering learning loss
## 463 He holds the Bible and was a citizen of Canada, or get out of business. Losing too much to get rid of Common Core--keep education local!
## 464 @SenWarren CAN YALL GET RID OF COMMON CORE THIS SHIT IS STRESSING ME THE TF OUT
## 465 “Conservation Education & Activism:\nDr. Dennis Liu of @EOWilsonFndtn on arming students to help our planet”\nLatest @PsychToday interview!\nhttps://t.co/eS8NHedg3L #conservation #HalfEarthProject #TellEWA https://t.co/AQhcUeaKbx
## 466 @DianaOrrock @D_Honch @NevadaPolicyRI @Icoacheducators @JillDickmanNV @Wheeler4Nevada @PKONeillNV Curious - what's wrong with common core?
## 467 @_chewyjs I call the committee common core..no one understands this shit
## 468 @JuddLegum Hey look, it’s time to switch to that common core math everyone’s talking about
## 469 Same word, different meaning: words like this are called homonyms– they're important for kids to learn!\n\nDownload our FREE homonyms practice worksheet from our resource library: https://t.co/ZqyAwKgdPQ\n\nCommon Core: CCSS.ELA-LITERACY.L.K.4.A, CCSS.ELA-LITERACY.L.1.4\n\n#homeschool https://t.co/7RddaTVMqW
## 470 What common core standard would this check off?? Asking for my eval. https://t.co/d2Qq0FUz1R
## 471 @naomirwolf @Cynthia62597760 Duh! He was behind common core, behind forced vaccines, behind pushing a plant based diet with his frankenmeats and buying up farmlands. He’s a psycho, a eugenics freak and wants to depopulate the earth. He is not a good man. Also refer to Event 201 scenario.
## 472 @WilliamYamamo18 @colhedoradeflor @RoxanneLaWin @j_cordeiro It's not that they need more history professors, they need to dump common core amd actually go back to...\nOh, I don't know, actually teaching kids instead of turning out mindless drones who passed a test.
## 473 @notZackr I rememebr my grade was the first to take the common core algebra regent LMAO
## 474 @TessaMakesLove Here's to the smart children who don't (and didn't) do that. And it's the problem with common core which wants kids to recite information from "reputable" sources...
## 475 @ConorCommentary @1Packersgurl @ARhyno2 My nephew is going to be 9 and he asked me to help him with his math Hw they do some common core stuff and I’m like ya I didn’t learn that way. It’s like 5 extra steps to get the same answer haha. https://t.co/tphV7VpNut
## 476 @spreadsheeticus @grand_handsome Welll - here in Florida the Governor banned it in public schools right before the Rona. So it’s still out there - California is still teaching Common Core.
## 477 @DrJohnMCole @RSBNetwork I agree 100% using common core math
## 478 It came in with Common Core https://t.co/ixmwmwDm3f
## 479 I’ve got a good idea on how common core math works because of my daughter. My 3rd grade son does math in a way I’ve never seen before, but he gets it right 95% of the time. I hate correcting him when he gets it right, but I don’t want to mess him up for the future. Suggestions?
## 480 @kkennedy38 @Space_Force1 @SackJD @shaunjean @MarcFink222 @Qynton @PaineInTheNeck Common core... https://t.co/jfbdTKsLBo
## 481 Spent three hours trying to do common core math today, then decided to bake chocolate cookies to help us both recover. I’m now having cookies and wine for dinner. That’s on par for pandemic life right?
## 482 @krupali @timesofindia So you just tweeted your tweet that was included in the Times of India? Is this a common core math question? 😀
## 483 @melissamiller33 @DFBHarvard Which is why they KNOW 138M people voted & Trump got 74.5M votes leaving only 63.5M but they BELIEVE(b/c MSM told them) buyden got 81M (totaling 155.5M or 17.5M more votes than voters! Must be that common core BS they've used since Obummer was in the WH
## 484 Of all the skills and hobbies I've accrued during the year+ lockdown, I'm most proud of the fact that I can finally do 3rd grade common core math! Still can't do the Floss, but we aren't past this pandemic yet! https://t.co/RDOWdOQgeB
## 485 @DerenicByrd Differences, yes, but there must also be respect and a shared common core that believes in the dignity of every human being.
## 486 @Uncreativegenus @DieterK58004402 @Missy1138 @CNN WRONG!!!!! \n\nSee how much more bettererer of a person I am...? I am using common core math!!!!!! \n\nCLEARLY I am morally superior!!!!!!!!!!!!!! Wait...I just put on another TWO masks!!
## 487 Common core - then we will never know the true answer.🤯🤯🤯 https://t.co/XbhATCz52t
## 488 @HotepJesus Exercise, business and 1st grade common core math 🤦♀️
## 489 @ChrisCoons @JohnBKing @AmeriCorps John King and his common core charter school cronies were a disaster for our schoolchildren way before anyone ever heard of COVID.
## 490 @d_ford_p @StatHunting See, common core is improving our understanding of real life math problems every day! https://t.co/TTYgkkSpcE
## 491 If You Liked Common Core, You're Going To Love Joe Biden's Civics https://t.co/bDNyxOJmaC
## 492 @bcteacher @rajboshmahal @Edu_Historian How? My son struggles with basic math as do many of his peers because of common core. They spend the whole year learning to pass a state test. I can count on one hand how many outstanding teachers my kids have had, combined, over the years. Then there’s the unions.
## 493 I figured out that most of the confusing common core strategies are there bc there is no longer a standard that kids have their math facts memorized.
## 494 @chuckwoolery Common Core math, obviously!
## 495 @Tactical_review A well regulated Militia, being necessary to the security of a free State, the right of the people to keep and bear Arms, shall not be infringed. Straight off the constitution. All they have to do is read. But common core does not permit reading.
## 496 @LABeachGal1 Common core......
## 497 @JustthefactsJJ @MCU_Direct Thats that new common core BS
## 498 throwback to when our teacher would show us cringe videos for common core and me and muskan would memorize them to perform them for our friends, lmfaooo
## 499 @grandmakrispy That is “Common Core Math” 😂
## 500 @nypost Teachers will soon be obsolete anyway. That's been the plan all along. Just look into Common Core and Individualized Learning and it's clear as day. Rotten to the Core.
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## 80 {0, 0, -1.2, 0, 0, -2.28142, 0.518, 0, 0, -2.4, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0}
## 81 {0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 82 {0, 0, 0, 0, 0, 0, 1.7, 0, 1.7, 0, 0, 0, 0}
## 83 {0, 0, 0, 0}
## 84 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
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## 86 {0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, -3.1}
## 87 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
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## 89 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 90 {0, 0, 0, 0, 0, 0, 0, 0}
## 91 {0, 0, 0, 0, 1.8, 0, 0, 0, 0, -0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 1.6, 0, 0, 0, 0, 0, 0, 0}
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## 93 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0}
## 94 {0, 0, 0, 0, 0, 0, 0, 0}
## 95 {0, 0, 0, 0, 0, 0, 0}
## 96 {0, 0, 0, 0, 0}
## 97 {0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0}
## 98 {0, 0, 0, 0, -2.1, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 99 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.133, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 100 {0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 2.9, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.7, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0}
## 101 {0, -0.8, 0, 0, 0, 0, -1.25, 0, 0, 0, 0, 0, 0, 0, 1.332, 0, 0, 1.95, 0, 0, 0, 0, 0, 0, -2.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 102 {0, 0, 1.8, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0}
## 103 {0, 0, 0}
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## 105 {0, 0, 0, 0, 0, 0, -3.1}
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## 108 {0, 1.1, -1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0}
## 109 {0, 0, 0, -2.1, 0, 0, 0, 0}
## 110 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 111 {0, 0, 0, 0, 2.193, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.702, 0, 0, 0, 0, 0, 0, 0, 0}
## 112 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 113 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 114 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 115 {0, 0, 0, 0, 0, 0, 0.6, -1.15, -1.25, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.45, 0, 0, 0, 0, -3.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 116 {0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 117 {0, 0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 118 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
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## 120 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0.1, 0, 0, 0}
## 121 {0, 0, 0, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 2.9895, 0, 0, 0, 0, 0}
## 122 {0, 0, 0, 0, 0, 0, 0, 0.3, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.036, 0, 0}
## 123 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, -2.7, 0, 0, 0, 0, 0, 0, 0, -1.998, 0, 0, 2.2, 0, 0, 0.3, 1.5, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 2.093, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 124 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, -1}
## 125 {0, 0, 0, 0, 0}
## 126 {0, 0, 0, 0, 0, 0, 0, 1.5, 0.3, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.1, 0, 0, 0, 0, 0}
## 127 {0, 0.7, 0, 0, 0, -0.6, 0, 0, 0, 0, 0, 0, 0, 2.25, 0, 0, 0, 0, 2.55, 0, 0, -3.45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.882475, 0, 0, 0, 0, 0, 0, 0}
## 128 {0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, -1.2, 0, 0}
## 129 {0, 0, 0, -2.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4}
## 130 {0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0}
## 131 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, -3.3, 0, 0, 0, 0, 0, 0}
## 132 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 1.3, 0, 0, 0, 0, 0}
## 133 {0, 2.45, 0, -1.3, 0, 0, -0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 2.45}
## 134 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.3, 0, 0, 0, 0, 0, 0, 0}
## 135 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.554, 0, -2.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 136 {0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 137 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 138 {0, 0.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.55, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0}
## 139 {0, 0, 0, 0, 0, -3.4}
## 140 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 141 {0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.993, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 1.6}
## 142 {0, 0, 0, 0, 0, 0, 0, 0}
## 143 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 144 {0, 0, 1.5, 0, 0, 0, 0, 0, 0}
## 145 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 146 {0, 0, 0, 0, 0, 0, -4.226, 0, 0, 0, 0}
## 147 {0, 0, -3.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.9, 0, 0, 0}
## 148 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 149 {0, 0, 0, 0, 0, 0, -1.7}
## 150 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6, 0, 0, 0, -1.258, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 151 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 152 {0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 153 {0, 0, 0, 0, 0, 0, 0, 0, 0, -0.4, 0, 0, 0, 0, 0, 0, 0, 0}
## 154 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.733, 0, 0, 0, 0}
## 155 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.77835, 0, 0, -1.6, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 156 {0, 0.75, 0, 0, 0, 0, 0.85, 0, 0, -3.45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.882475, 0, 0, 0, 0, 0, 0, 0}
## 157 {0, 0, 0, 0}
## 158 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0}
## 159 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 160 {0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 2}
## 161 {0, 0.75, 0, 0, 0, 0, 0.85, 0, 0, -3.45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.882475, 0, 0, 0, 0, 0, 0, 0}
## 162 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 163 {-0.7, 0, 1.7, 0, 0, 0, 2.293, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 164 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 165 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 166 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.4, -2.1, 0, 0, 0, -1.8, 0}
## 167 {0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 168 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0}
## 169 {0, 0, 0, 0, 0, 0, 0.777, 0, 0, 0, -0.88185, 0, 0, 0, 0, -1.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 170 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 171 {0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0}
## 172 {0, 1.1, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 173 {0, 0, -2.6, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 174 {0, 0, 0, 0, 0, -2.37835, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.924, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.7637, 0, 0, 0}
## 175 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 176 {0, 0, 1.8, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0}
## 177 {0, 0, 1.3, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, -3.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, -1.9}
## 178 {0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 179 {0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 180 {0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 181 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 182 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.9, 0, 0}
## 183 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0}
## 184 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0}
## 185 {0, 0, 0, 0, -2.6, 0, -0.6, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.258, 0, 0, 0, 0, 0, 0, 0, 0}
## 186 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 187 {0, 0, 0, 0}
## 188 {0, 0, 0, 0}
## 189 {0, 0, 0, -0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 190 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.593, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 1.5, 0}
## 191 {0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.593, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 192 {0, 0, 0, 0, 0, -3.1, 0}
## 193 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 194 {0, 0, 0, 0, 0, 0, 0}
## 195 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 196 {0, 0, 0, 0, 1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0}
## 197 {0, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 2.7}
## 198 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.95, -0.75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 199 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 200 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 201 {0, 0, 0, 0, 0, 0, 0, 0, 0, -2.5, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 1.97835, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0}
## 202 {0, 0, 0, 0}
## 203 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 204 {0, 0, 0, 0, 0, 0, 0, 0, 0, -3.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2}
## 205 {0, 0, 2.193, 0, 0, 0, 0, 0, 0, 0}
## 206 {0, 0, 0, 0}
## 207 {0, 0, 0, 0, 0, 0, 0, -1.87835, 0, 0, 0, 0, 0, 0, 0, -0.4, 0, -3.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0}
## 208 {0, 0, 0, 0, 0}
## 209 {0, 0, 0, 0, 0, -2.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, -3, 0, 0, 0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3, 0}
## 210 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 211 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.6, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 212 {0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0}
## 213 {0, 0, 0, 0, 0, 0, -2.707, 0, 0, 0, 0, 0, 0}
## 214 {0, 0, 0, 0, 0, 0, 0, 0, 0, 2.9, 1.9, 0, 0, 0, 0, 2.9, -3.4, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 215 {0, 0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 216 {0, 0, 0, 0}
## 217 {0, 0, 0, 0, 0, 0, 0}
## 218 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.493, 0}
## 219 {0, 0, -2.5, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 0, 0, 0, 0, 0, -4.433, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.554, 0, 0, 0, 0}
## 220 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 221 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 222 {0, 0, 0, 0, 1.3, 0, 0, -1.11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 223 {0, 0, 0, 0, 0, 0, 0, -2.7, 0, 0, 0, 0, 0, 0, 0, 0}
## 224 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 225 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 226 {0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 2.3, 0, 0, 0, 0}
## 227 {0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.184, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0}
## 228 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.333, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 229 {-2.3, 0, 0, 0, 0, 0, 0, 0}
## 230 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 231 {0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 232 {0, 0, 0, 0, 0}
## 233 {0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 234 {0, 0, 0, 0, 0}
## 235 {0, 0, 0, 0, 0, 0, 0, 0}
## 236 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.633, 0, 0, 0, -2.7, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 237 {0, 0, 0, 0, 0, 0, 0, 0}
## 238 {0, 0, 0, 0, 1.3, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 239 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 240 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0.75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4.65, -2.55, 0, 0, 0, 0, 0, 0, 0, -2.25, 0, 2.7, 0, 0, 0, 0, 0, 3.3, 0, 3, 0, 2.55, 0, 0, 3.45, 0, 0, 2.1, 0}
## 241 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 242 {0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, -2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 243 {0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.4, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 1, 0}
## 244 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 245 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 246 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 247 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 248 {0, 0, 0, 0, 0}
## 249 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 250 {0, 0, 0, 0, 0, 0, 0}
## 251 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, -2.7, 0, -3.5, 0, 0, 0, -1.4, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, -0.9, 0, 0, 0}
## 252 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 253 {0, 0, 0, 0, 0, 0, 0}
## 254 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 255 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.11, 0, 0, 0, 0, 0, 0, 0, 0}
## 256 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 257 {0, 0, 0, 0, 0, 0}
## 258 {0, 0, 0, 0, 0, 0, -3.1, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0.962, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0}
## 259 {0, 2.35, 0, 0, 0}
## 260 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 261 {0, 0, -2.8, 0}
## 262 {0, 0, 3.2, 0, 0, 0, 0, 1.258, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0}
## 263 {0, 0, 0, 0, 0, 0, 0}
## 264 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 265 {0, 0, 0, 0, 0, 0, 0, 1.1, 0}
## 266 {0, 0, 0, 0, 0}
## 267 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 268 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 269 {0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0}
## 270 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 271 {-1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 272 {0, 0, 0, 0, 0, 0, 0, 3.493, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, -1.7, -1.2, 0, 0, 0, -1.8, 0, 0, -1.3}
## 273 {0, 0, 0, 0, 0, 0}
## 274 {0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 275 {0, 0, 0, 0, 0, 0}
## 276 {0, 0, 0, 0}
## 277 {0, 0, 0, 0}
## 278 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.193, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, -2.5, 0, -0.4, 0, 0, 0}
## 279 {0, 0, 0, 0}
## 280 {0, 0, 0, 0, 0, 0, 0, 0}
## 281 {0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0}
## 282 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 283 {0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0}
## 284 {0, 0, 0, 0, 0, 0}
## 285 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 286 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.258, 0, 0, 0, 0, 0, 0.8, 2.6, 0, 0, 0, 0, 0, 0, 0}
## 287 {0, 0, 0, 2.9, 0, 0, 0, 0, -2.3, -2.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1}
## 288 {0, 0, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.85, 0, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, -2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 289 {0, 0, 0, 0, 0, 0}
## 290 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 291 {0, 0, 0, 0, 0, 2.2, 0, 0, 0, 2.2, 1.3, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 0}
## 292 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 293 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.293, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 294 {0, 0, 0, -0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4.8, 4.6245}
## 295 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.554, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 296 {0, 0, 0, 0, 0}
## 297 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.6, 0, 0, 0}
## 298 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 299 {0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 2.17835, 0, 0, 0, 0, 0}
## 300 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0}
## 301 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0}
## 302 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.25, 0, 0, 0, -2.85}
## 303 {0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0}
## 304 {0, 0, 1, 0, 0, 0, 0, -2.2, 0}
## 305 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, -0.9, 0}
## 306 {0, 0, -1, 0, 0, 0, 1.7, 0, 0, 0, -0.4, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 2.1}
## 307 {0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.1, 0, 0}
## 308 {0, 0, 0, 0, 0}
## 309 {0, 0, 0, 0, 0, 0, -1.5, 0}
## 310 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 1.3, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 311 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 312 {0, 0, 0, 0}
## 313 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 314 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 315 {0, 0, 0, 0, 0.1, 0, 0}
## 316 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 317 {0, 0, 0, 0}
## 318 {0, 0, 0, 0, 0, 0, 0}
## 319 {0, 0, 0, 0, 0}
## 320 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 321 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 322 {0.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 323 {0.75, 0, 1, 0, 0, 0, 0, 0, 0, -0.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.1995, 0, 0}
## 324 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, -1.7, 0, 1.6, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0}
## 325 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 326 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 327 {0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 328 {0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 329 {0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 330 {0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0}
## 331 {0, 0, 0, 0, 0, 0, -2.6, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0}
## 332 {0, 0, 0, 0}
## 333 {0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.47835, 0, 0, 0, 0, 0, 0, 0}
## 334 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 335 {0, 0.666, 0, 0, 2.3, 0, 0, 0, 0, 0, 0, 0}
## 336 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 1.1, 0}
## 337 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 338 {0, 0, 0, 0, 2.35, 0, 0, 0, 0, 0, 0}
## 339 {0, 0, 0, 0, 0, 2, 0, 0, 1.1, 0, 0, 0}
## 340 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.184, 0, 0, 0, 0, 0, 1.924, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 341 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 342 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.2, 0, 0, 0, 0, 0, 0, 0, 1.3}
## 343 {0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0}
## 344 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.5, 0, 0, 0, 0}
## 345 {0, 0, 0, 0, 0, 1.4, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 346 {0, 0, 0, 0, 0, 0, 0}
## 347 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.17835, 0, 0, 0, 0, 0}
## 348 {0, 0, 0, 0, 0}
## 349 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 350 {0, 0, 0, 0, 0, 0, 0}
## 351 {0, 0, 0, 0, 0, 0}
## 352 {0, 0, 0, 0}
## 353 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 354 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.793, 0, 0, 0, 0, 0, 1.5, 0, 0, 0}
## 355 {0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2}
## 356 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 357 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 358 {0, 3.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 1.9, -3.4, 0, -1.8, 0, 0, 0, 0, 0, 0}
## 359 {0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, -2.6, 0}
## 360 {0, 0, 0, 0, 0, -2.6, 0, 0}
## 361 {0, 0, 0, -2.1, 0, 0, 0, 0}
## 362 {0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 363 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 364 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.193, 0, 0, 0}
## 365 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 366 {0, 2.3, 0, 0, 0, 0, 0, 0, 0, 1.1, 0}
## 367 {1.5165, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.45, 0, 0, 0, 0, 0, 0, 1.35, 0, 0, 0, 0, 0, 0, 0}
## 368 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 369 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 370 {0, 0, 0, 0, 0, 0, 0.888, 0, 0, 0, 0, 0, 0, 1.5, 0, 2.8, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0}
## 371 {0, 0, 0, 0, 0, 0, 0, 0, 2.333, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, -1.702, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, -1.702, 0, -1.258, 0, 0}
## 372 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 373 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 374 {0, 0, 0, 0.75, 0, 0, 0, 0, 0, 0, 3.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.85, 0}
## 375 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 376 {0, 2.9, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 1.8, 0, 0, 0}
## 377 {0, 0, 0, 0, 0, 0, 0, 0.75, 0, 0, 0, 0, 0.8965, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0}
## 378 {0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 379 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.8, 0, 0, 0, 0}
## 380 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.4}
## 381 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0}
## 382 {0, 0, 0, 0}
## 383 {0, 0, 0, 0.8, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 384 {0, 0, 0, 0, 0, 0, 0}
## 385 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.7, 0, 0, 0}
## 386 {0, 0, 0, 0, 0, 3.2, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.2, 0}
## 387 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 388 {0, 0, 0, 0, 0, -3.4, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.093}
## 389 {0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 390 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 391 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.888, 0, 0, 0}
## 392 {3.1, 0, -0.3, 0, 0, 2.2, 0, 1.093, 0, 2.1637, 0, 0, 2.3, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 393 {0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0.37, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0}
## 394 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 395 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 396 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.554, 0, 0, 0, 0, 0, 0, 0}
## 397 {0, 0, 0, 0, 0.75, 0, 0.45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.95}
## 398 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 399 {0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.8, 0, 0, 0, 0, 0, 0, 0}
## 400 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.6, 0, 0, 0, 0, -2.5}
## 401 {0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 402 {0, 0, 0, 0, 0, 0, 0, -2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 1.6, 0, 0}
## 403 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 0, 0, 0, -5.5395, 2.667525, 0, 0, 0, 0, 0, 0, 2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 404 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.5, 0, 0}
## 405 {0, 0, 0, 0, 0, -2.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, -1.7, 0, 0}
## 406 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 407 {0, 0, 0, 0, 0, -1.2, 0, 0, 0, -2.6, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, -2.093, 0, 0, 0, 0, 0, 0, 0, 0, -2.893, 0, 0, 0, 0}
## 408 {0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 409 {0, 0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0}
## 410 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 411 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 412 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 413 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 414 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, 2.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 415 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 416 {0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, -3.3, -2.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.6, 0, 0, 0, -1.2, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, -2.57835, -2.8637, 2.35}
## 417 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0}
## 418 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, -0.4, 0, 0, -0.4, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0}
## 419 {0, 0, 0, 0}
## 420 {0, 0, 0, 0, 0, 0, 0}
## 421 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4.05, 0, 1.8, 0, 0, 0, 0, -2.55, 0, 0, 2.55, 1.5}
## 422 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 423 {0, 0, 0, 0, 0, -2.2, 0, 0, 0, 0, 0, 0}
## 424 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 425 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.9, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 426 {0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 427 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 428 {0, 0, 0}
## 429 {0, 0, 1.5, 0, 0, 0, 0, 0, 0, -2.693}
## 430 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 431 {0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.6, 0}
## 432 {0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.6, 0, 0, 0, 0, 0, 0, 0, -2.6, 0, 0, 0, 0, 0, 0}
## 433 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.4}
## 434 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 435 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.5, 0, -2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3}
## 436 {0, 0, 0, 0, -0.3, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0}
## 437 {0, 0, 1.8, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0}
## 438 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 439 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 440 {0, 0, 1.2, 3.2, 0, 0, 0, 0, 0, 0, 0}
## 441 {0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0}
## 442 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 443 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 444 {0, 0, 0, 0, 0, 0.629, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.55, 0, 0, 0, 0, 0, 0, 0, 0, -3.5895, 0, 0, 0, 0, 0, -3.6, 2.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3}
## 445 {0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 2.233, 1.833, 0, 2.633}
## 446 {0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 447 {0, 0, 0, 0}
## 448 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 449 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0}
## 450 {0, 0, 0, 0, 0, 0, -1.6, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 451 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 452 {0, 0, 0, 1.1, 0, 0}
## 453 {0, 0, 0, 0, 0, 0.888, 0, 0.888, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 454 {0, 0, 0, 0, 0, 0, 2.35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.406, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 455 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 456 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 457 {0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0}
## 458 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.29082, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 459 {0, 0, 0, 0, 0}
## 460 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 461 {1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0}
## 462 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.554, 0, 0.962}
## 463 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 464 {0, 0, 0, 0, 0, 0, 0, 0, 0, -3.333, 0, -2.233, 0, 0, 0, 0}
## 465 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 466 {0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, -2.1, 0, 0, 0}
## 467 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.6}
## 468 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 469 {0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 3.033, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 470 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 471 {0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.406, 0, 0, 0, 0, 0, 0, 0}
## 472 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.9, 0, 0, 0, 0, 0}
## 473 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.183}
## 474 {0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 475 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0}
## 476 {0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 477 {0, 0, 0, 1.5, 0, 0, 0, 0, 0}
## 478 {0, 0, 0, 0, 0, 0, 0}
## 479 {0, 0, 0, 0.95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -4.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.45, 0, -2.25, 0, 0, 0, 0, 0, 0}
## 480 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 481 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 482 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 483 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 484 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1965, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 485 {0, 0, 0.85, 0, 0, 0, 0, 0, 3.15, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 2.55, 0, 0, 0, 0}
## 486 {0, 0, 0, 0, -2.833, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.433, 0, 0, 0, 2.5, 0, 0, 0, 0, 0, 0, 0}
## 487 {0, 0, 0, 0, 0, 0, 0, 0, 0, -1.332, 0, 0}
## 488 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 489 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 490 {0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0}
## 491 {0, 0, 1.8, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0}
## 492 {0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 493 {0, 0, 0, 0, 0, 0, 0, -1.1637, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 494 {0, 0, 0, 0, 0}
## 495 {0, 0, 0.55, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 1.15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.333, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 496 {0, 0, 0}
## 497 {0, 0, 0, 0, 0, 0, 0, 0}
## 498 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0}
## 499 {0, 0, 0, 0, 0, 0, 0}
## 500 {0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, -2.3, 0, 0, 0}
## compound pos neu neg but_count
## 1 0.000 0.000 1.000 0.000 0
## 2 -0.296 0.000 0.956 0.044 0
## 3 0.000 0.000 1.000 0.000 0
## 4 0.000 0.000 1.000 0.000 0
## 5 0.380 0.130 0.778 0.092 0
## 6 0.000 0.000 1.000 0.000 0
## 7 0.000 0.000 1.000 0.000 0
## 8 -0.885 0.053 0.726 0.221 1
## 9 -0.477 0.000 0.693 0.307 0
## 10 0.440 0.172 0.828 0.000 0
## 11 0.000 0.000 1.000 0.000 0
## 12 0.000 0.000 1.000 0.000 2
## 13 0.788 0.190 0.768 0.041 0
## 14 0.000 0.000 1.000 0.000 0
## 15 0.000 0.000 1.000 0.000 0
## 16 -0.550 0.119 0.637 0.244 0
## 17 -0.340 0.101 0.752 0.147 0
## 18 0.000 0.000 1.000 0.000 0
## 19 -0.440 0.000 0.775 0.225 0
## 20 0.000 0.000 1.000 0.000 0
## 21 0.494 0.156 0.761 0.083 0
## 22 0.000 0.000 1.000 0.000 0
## 23 -0.377 0.000 0.834 0.166 0
## 24 -0.691 0.000 0.705 0.295 0
## 25 0.000 0.000 1.000 0.000 0
## 26 0.250 0.125 0.875 0.000 0
## 27 0.000 0.000 1.000 0.000 0
## 28 -0.813 0.120 0.496 0.384 0
## 29 -0.052 0.000 0.934 0.066 0
## 30 -0.052 0.056 0.885 0.060 0
## 31 0.273 0.068 0.932 0.000 0
## 32 0.000 0.000 1.000 0.000 0
## 33 0.000 0.000 1.000 0.000 1
## 34 -0.844 0.061 0.741 0.198 1
## 35 0.458 0.131 0.820 0.049 0
## 36 -0.511 0.000 0.798 0.202 0
## 37 0.000 0.000 1.000 0.000 0
## 38 -0.439 0.035 0.884 0.081 0
## 39 -0.250 0.114 0.721 0.164 0
## 40 0.477 0.406 0.594 0.000 0
## 41 0.585 0.166 0.767 0.066 0
## 42 0.000 0.000 1.000 0.000 0
## 43 -0.831 0.095 0.556 0.349 0
## 44 -0.691 0.060 0.754 0.186 0
## 45 -0.818 0.025 0.817 0.158 0
## 46 -0.601 0.037 0.873 0.089 0
## 47 0.000 0.000 1.000 0.000 0
## 48 0.000 0.000 1.000 0.000 0
## 49 0.778 0.315 0.685 0.000 0
## 50 0.000 0.000 1.000 0.000 0
## 51 0.439 0.182 0.818 0.000 0
## 52 -0.774 0.068 0.679 0.253 0
## 53 0.000 0.000 1.000 0.000 0
## 54 -0.511 0.000 0.809 0.191 0
## 55 0.115 0.220 0.590 0.190 0
## 56 0.000 0.000 1.000 0.000 0
## 57 -0.340 0.000 0.625 0.375 0
## 58 0.000 0.000 1.000 0.000 0
## 59 -0.713 0.000 0.901 0.099 1
## 60 0.000 0.000 1.000 0.000 0
## 61 0.000 0.000 1.000 0.000 0
## 62 0.799 0.132 0.868 0.000 1
## 63 0.077 0.038 0.962 0.000 0
## 64 0.938 0.491 0.509 0.000 0
## 65 0.637 0.139 0.811 0.051 0
## 66 -0.340 0.000 0.769 0.231 0
## 67 0.000 0.000 1.000 0.000 0
## 68 -0.727 0.000 0.681 0.319 0
## 69 -0.262 0.043 0.886 0.071 1
## 70 0.746 0.232 0.655 0.113 2
## 71 -0.340 0.000 0.806 0.194 0
## 72 0.000 0.000 1.000 0.000 0
## 73 0.000 0.000 1.000 0.000 0
## 74 0.642 0.094 0.906 0.000 0
## 75 0.340 0.255 0.745 0.000 0
## 76 -0.103 0.000 0.968 0.032 0
## 77 0.531 0.156 0.714 0.130 0
## 78 -0.456 0.130 0.681 0.189 0
## 79 -0.392 0.000 0.955 0.045 1
## 80 -0.505 0.140 0.697 0.163 0
## 81 0.273 0.077 0.923 0.000 0
## 82 0.660 0.329 0.671 0.000 0
## 83 0.000 0.000 1.000 0.000 0
## 84 0.000 0.000 1.000 0.000 0
## 85 -0.717 0.046 0.809 0.145 0
## 86 -0.735 0.000 0.721 0.279 0
## 87 0.000 0.000 1.000 0.000 0
## 88 0.554 0.188 0.734 0.078 0
## 89 0.000 0.000 1.000 0.000 0
## 90 0.000 0.000 1.000 0.000 0
## 91 0.000 0.121 0.737 0.143 0
## 92 -0.782 0.051 0.773 0.175 1
## 93 0.273 0.116 0.884 0.000 0
## 94 0.000 0.000 1.000 0.000 0
## 95 0.000 0.000 1.000 0.000 0
## 96 0.000 0.000 1.000 0.000 0
## 97 -0.511 0.000 0.732 0.268 0
## 98 -0.202 0.091 0.787 0.122 0
## 99 -0.629 0.000 0.863 0.137 0
## 100 -0.200 0.152 0.683 0.165 0
## 101 -0.385 0.094 0.765 0.141 1
## 102 0.791 0.389 0.611 0.000 0
## 103 0.000 0.000 1.000 0.000 0
## 104 0.000 0.000 1.000 0.000 0
## 105 -0.659 0.000 0.577 0.423 0
## 106 -0.579 0.090 0.752 0.159 1
## 107 0.296 0.041 0.959 0.000 0
## 108 0.273 0.167 0.736 0.097 0
## 109 -0.477 0.000 0.693 0.307 0
## 110 -0.402 0.000 0.899 0.101 0
## 111 0.709 0.219 0.781 0.000 0
## 112 0.421 0.074 0.926 0.000 0
## 113 0.000 0.000 1.000 0.000 0
## 114 0.000 0.000 1.000 0.000 0
## 115 -0.933 0.025 0.736 0.240 1
## 116 0.681 0.196 0.804 0.000 0
## 117 0.511 0.099 0.901 0.000 0
## 118 0.000 0.000 1.000 0.000 0
## 119 0.000 0.000 1.000 0.000 0
## 120 0.382 0.114 0.886 0.000 0
## 121 0.680 0.237 0.763 0.000 1
## 122 -0.017 0.063 0.891 0.046 0
## 123 0.557 0.210 0.652 0.138 0
## 124 -0.511 0.000 0.807 0.193 0
## 125 0.000 0.000 1.000 0.000 0
## 126 0.840 0.278 0.722 0.000 0
## 127 0.746 0.232 0.655 0.113 2
## 128 -0.660 0.041 0.806 0.153 0
## 129 -0.832 0.000 0.820 0.180 0
## 130 -0.340 0.000 0.806 0.194 0
## 131 -0.822 0.000 0.833 0.167 0
## 132 0.670 0.256 0.744 0.000 0
## 133 0.802 0.199 0.723 0.078 0
## 134 0.687 0.094 0.906 0.000 1
## 135 -0.714 0.000 0.883 0.117 0
## 136 0.511 0.081 0.919 0.000 0
## 137 0.000 0.000 1.000 0.000 0
## 138 0.214 0.097 0.821 0.081 1
## 139 -0.660 0.000 0.532 0.468 0
## 140 0.000 0.000 1.000 0.000 0
## 141 0.764 0.165 0.782 0.053 0
## 142 0.000 0.000 1.000 0.000 0
## 143 -0.511 0.000 0.907 0.093 0
## 144 0.361 0.238 0.762 0.000 0
## 145 0.361 0.094 0.906 0.000 0
## 146 -0.817 0.000 0.607 0.393 0
## 147 -0.864 0.000 0.675 0.325 0
## 148 0.000 0.000 1.000 0.000 0
## 149 -0.402 0.000 0.690 0.310 0
## 150 -0.594 0.000 0.892 0.108 0
## 151 0.000 0.000 1.000 0.000 0
## 152 0.706 0.131 0.869 0.000 0
## 153 -0.193 0.000 0.906 0.094 0
## 154 0.577 0.211 0.789 0.000 0
## 155 -0.641 0.038 0.848 0.114 0
## 156 0.258 0.203 0.677 0.120 1
## 157 0.000 0.000 1.000 0.000 0
## 158 -0.625 0.052 0.811 0.137 0
## 159 0.000 0.000 1.000 0.000 0
## 160 0.735 0.408 0.592 0.000 0
## 161 0.258 0.203 0.677 0.120 1
## 162 0.000 0.000 1.000 0.000 0
## 163 0.648 0.253 0.675 0.072 0
## 164 0.000 0.000 1.000 0.000 0
## 165 0.166 0.035 0.965 0.000 1
## 166 -0.852 0.000 0.807 0.193 1
## 167 0.831 0.201 0.799 0.000 0
## 168 -0.318 0.000 0.952 0.048 0
## 169 -0.191 0.056 0.876 0.069 1
## 170 0.000 0.000 1.000 0.000 0
## 171 0.818 0.179 0.821 0.000 0
## 172 0.382 0.114 0.886 0.000 0
## 173 -0.250 0.117 0.721 0.162 0
## 174 -0.786 0.050 0.783 0.167 0
## 175 0.000 0.000 1.000 0.000 0
## 176 0.791 0.389 0.611 0.000 0
## 177 -0.757 0.085 0.743 0.172 0
## 178 0.637 0.276 0.724 0.000 0
## 179 -0.402 0.000 0.856 0.144 0
## 180 -0.421 0.000 0.851 0.149 0
## 181 0.000 0.000 1.000 0.000 0
## 182 -0.440 0.000 0.909 0.091 0
## 183 -0.250 0.050 0.863 0.086 0
## 184 0.494 0.099 0.901 0.000 0
## 185 -0.887 0.000 0.779 0.221 0
## 186 0.000 0.000 1.000 0.000 0
## 187 0.000 0.000 1.000 0.000 0
## 188 0.000 0.000 1.000 0.000 0
## 189 -0.484 0.000 0.889 0.111 0
## 190 -0.432 0.116 0.729 0.155 0
## 191 0.272 0.090 0.862 0.048 0
## 192 -0.625 0.000 0.594 0.406 0
## 193 0.000 0.000 1.000 0.000 0
## 194 0.000 0.000 1.000 0.000 0
## 195 0.000 0.000 1.000 0.000 0
## 196 0.778 0.315 0.685 0.000 0
## 197 -0.654 0.089 0.695 0.217 0
## 198 0.052 0.042 0.921 0.037 1
## 199 0.000 0.000 1.000 0.000 0
## 200 0.000 0.000 1.000 0.000 0
## 201 0.795 0.222 0.710 0.067 0
## 202 0.000 0.000 1.000 0.000 0
## 203 0.077 0.101 0.810 0.089 0
## 204 -0.858 0.000 0.774 0.226 0
## 205 0.540 0.279 0.721 0.000 0
## 206 0.000 0.000 1.000 0.000 0
## 207 -0.610 0.080 0.733 0.187 0
## 208 0.000 0.000 1.000 0.000 0
## 209 -0.961 0.000 0.678 0.322 0
## 210 0.000 0.000 1.000 0.000 0
## 211 0.273 0.101 0.835 0.064 0
## 212 0.273 0.174 0.826 0.000 0
## 213 -0.612 0.000 0.750 0.250 0
## 214 0.494 0.248 0.579 0.174 0
## 215 0.511 0.191 0.809 0.000 0
## 216 0.000 0.000 1.000 0.000 0
## 217 0.000 0.000 1.000 0.000 0
## 218 -0.541 0.000 0.863 0.137 0
## 219 -0.757 0.101 0.706 0.193 0
## 220 0.000 0.000 1.000 0.000 0
## 221 0.000 0.000 1.000 0.000 0
## 222 0.049 0.065 0.875 0.060 0
## 223 -0.572 0.000 0.802 0.198 0
## 224 -0.440 0.000 0.917 0.083 0
## 225 0.000 0.000 1.000 0.000 0
## 226 0.556 0.166 0.763 0.071 0
## 227 0.300 0.085 0.872 0.042 0
## 228 -0.652 0.000 0.900 0.100 0
## 229 -0.511 0.000 0.680 0.320 0
## 230 0.000 0.000 1.000 0.000 0
## 231 -0.477 0.000 0.866 0.134 0
## 232 0.000 0.000 1.000 0.000 0
## 233 0.077 0.040 0.960 0.000 0
## 234 0.000 0.000 1.000 0.000 0
## 235 0.000 0.000 1.000 0.000 0
## 236 0.655 0.198 0.711 0.091 0
## 237 0.000 0.000 1.000 0.000 0
## 238 -0.026 0.089 0.817 0.093 0
## 239 -0.448 0.000 0.940 0.060 0
## 240 0.977 0.405 0.505 0.090 1
## 241 -0.691 0.000 0.882 0.118 0
## 242 -0.649 0.046 0.829 0.125 0
## 243 0.580 0.160 0.804 0.036 0
## 244 0.000 0.000 1.000 0.000 0
## 245 0.000 0.000 1.000 0.000 0
## 246 0.000 0.000 1.000 0.000 0
## 247 0.459 0.064 0.936 0.000 0
## 248 0.000 0.000 1.000 0.000 0
## 249 0.000 0.000 1.000 0.000 0
## 250 0.000 0.000 1.000 0.000 0
## 251 -0.955 0.000 0.612 0.388 0
## 252 0.318 0.029 0.971 0.000 0
## 253 0.000 0.000 1.000 0.000 0
## 254 0.000 0.000 1.000 0.000 0
## 255 -0.355 0.000 0.895 0.105 0
## 256 0.525 0.091 0.909 0.000 0
## 257 0.000 0.000 1.000 0.000 0
## 258 -0.447 0.077 0.812 0.111 0
## 259 0.519 0.456 0.544 0.000 0
## 260 0.000 0.000 1.000 0.000 0
## 261 -0.586 0.000 0.441 0.559 0
## 262 0.738 0.241 0.690 0.069 0
## 263 0.000 0.000 1.000 0.000 0
## 264 0.572 0.104 0.896 0.000 0
## 265 0.273 0.208 0.792 0.000 0
## 266 0.000 0.000 1.000 0.000 0
## 267 0.000 0.000 1.000 0.000 0
## 268 0.000 0.000 1.000 0.000 0
## 269 0.226 0.160 0.840 0.000 0
## 270 -0.176 0.000 0.947 0.053 0
## 271 0.000 0.070 0.859 0.070 0
## 272 -0.783 0.114 0.648 0.238 0
## 273 0.000 0.000 1.000 0.000 0
## 274 0.599 0.126 0.874 0.000 0
## 275 0.000 0.000 1.000 0.000 0
## 276 0.000 0.000 1.000 0.000 0
## 277 0.000 0.000 1.000 0.000 0
## 278 -0.573 0.074 0.743 0.183 0
## 279 0.000 0.000 1.000 0.000 0
## 280 0.000 0.000 1.000 0.000 0
## 281 -0.456 0.000 0.843 0.157 0
## 282 0.000 0.000 1.000 0.000 0
## 283 -0.273 0.000 0.811 0.189 0
## 284 0.000 0.000 1.000 0.000 0
## 285 0.000 0.000 1.000 0.000 0
## 286 0.484 0.156 0.779 0.065 0
## 287 -0.748 0.100 0.638 0.262 0
## 288 -0.450 0.079 0.798 0.123 2
## 289 0.000 0.000 1.000 0.000 0
## 290 0.000 0.000 1.000 0.000 1
## 291 0.908 0.242 0.758 0.000 0
## 292 -0.052 0.041 0.914 0.045 0
## 293 -0.509 0.000 0.909 0.091 0
## 294 0.916 0.346 0.606 0.048 1
## 295 0.631 0.090 0.910 0.000 0
## 296 0.000 0.000 1.000 0.000 0
## 297 0.557 0.110 0.890 0.000 0
## 298 0.000 0.000 1.000 0.000 0
## 299 0.657 0.264 0.736 0.000 0
## 300 0.361 0.106 0.894 0.000 0
## 301 0.250 0.065 0.935 0.000 0
## 302 -0.612 0.092 0.704 0.204 1
## 303 -0.557 0.000 0.796 0.204 0
## 304 -0.296 0.164 0.574 0.262 0
## 305 -0.637 0.000 0.807 0.193 0
## 306 0.340 0.192 0.629 0.179 0
## 307 0.250 0.085 0.851 0.064 0
## 308 0.000 0.000 1.000 0.000 0
## 309 -0.361 0.000 0.737 0.263 0
## 310 0.077 0.105 0.796 0.099 0
## 311 -0.128 0.000 0.963 0.037 0
## 312 0.000 0.000 1.000 0.000 0
## 313 0.000 0.000 1.000 0.000 0
## 314 0.000 0.000 1.000 0.000 0
## 315 0.026 0.155 0.845 0.000 0
## 316 0.000 0.000 1.000 0.000 0
## 317 0.000 0.000 1.000 0.000 0
## 318 0.000 0.000 1.000 0.000 0
## 319 0.000 0.000 1.000 0.000 0
## 320 -0.494 0.000 0.918 0.082 0
## 321 0.000 0.000 1.000 0.000 0
## 322 0.141 0.033 0.967 0.000 1
## 323 -0.392 0.124 0.697 0.179 1
## 324 -0.681 0.082 0.660 0.258 0
## 325 0.000 0.000 1.000 0.000 0
## 326 0.000 0.000 1.000 0.000 0
## 327 -0.557 0.000 0.909 0.091 0
## 328 0.340 0.146 0.854 0.000 0
## 329 -0.223 0.044 0.897 0.059 0
## 330 -0.691 0.000 0.814 0.186 0
## 331 -0.052 0.147 0.733 0.120 0
## 332 0.000 0.000 1.000 0.000 0
## 333 -0.569 0.000 0.900 0.100 0
## 334 0.000 0.000 1.000 0.000 0
## 335 0.608 0.332 0.668 0.000 0
## 336 0.026 0.070 0.864 0.066 0
## 337 0.000 0.000 1.000 0.000 0
## 338 0.519 0.251 0.749 0.000 0
## 339 0.625 0.338 0.662 0.000 0
## 340 0.660 0.103 0.897 0.000 0
## 341 0.000 0.000 1.000 0.000 0
## 342 -0.226 0.098 0.766 0.136 0
## 343 0.509 0.228 0.660 0.112 0
## 344 -0.542 0.000 0.885 0.115 0
## 345 -0.226 0.071 0.860 0.069 0
## 346 0.000 0.000 1.000 0.000 0
## 347 0.490 0.105 0.895 0.000 0
## 348 0.000 0.000 1.000 0.000 0
## 349 0.000 0.000 1.000 0.000 0
## 350 0.000 0.000 1.000 0.000 0
## 351 0.000 0.000 1.000 0.000 0
## 352 0.000 0.000 1.000 0.000 0
## 353 0.000 0.000 1.000 0.000 0
## 354 0.686 0.220 0.780 0.000 0
## 355 0.743 0.223 0.777 0.000 0
## 356 -0.572 0.000 0.913 0.087 1
## 357 0.000 0.000 1.000 0.000 0
## 358 0.869 0.276 0.614 0.110 0
## 359 -0.700 0.000 0.691 0.309 0
## 360 -0.557 0.000 0.660 0.340 0
## 361 -0.477 0.000 0.693 0.307 0
## 362 -0.511 0.000 0.809 0.191 0
## 363 0.000 0.000 1.000 0.000 0
## 364 0.493 0.166 0.834 0.000 0
## 365 0.000 0.000 1.000 0.000 0
## 366 0.660 0.375 0.625 0.000 0
## 367 0.715 0.190 0.810 0.000 1
## 368 0.000 0.000 1.000 0.000 0
## 369 0.000 0.000 1.000 0.000 0
## 370 0.680 0.250 0.671 0.079 0
## 371 -0.325 0.131 0.701 0.168 0
## 372 0.000 0.000 1.000 0.000 0
## 373 -0.477 0.000 0.919 0.081 0
## 374 0.696 0.210 0.701 0.088 2
## 375 0.000 0.000 1.000 0.000 0
## 376 0.878 0.437 0.563 0.000 0
## 377 0.675 0.115 0.857 0.028 1
## 378 0.439 0.076 0.924 0.000 0
## 379 0.586 0.142 0.858 0.000 0
## 380 -0.527 0.000 0.793 0.207 0
## 381 0.273 0.160 0.840 0.000 0
## 382 0.000 0.000 1.000 0.000 0
## 383 0.718 0.160 0.798 0.043 0
## 384 0.000 0.000 1.000 0.000 0
## 385 -0.178 0.000 0.922 0.078 0
## 386 0.557 0.119 0.825 0.056 0
## 387 0.000 0.000 1.000 0.000 0
## 388 0.294 0.165 0.725 0.110 0
## 389 0.511 0.248 0.752 0.000 0
## 390 0.353 0.057 0.943 0.000 1
## 391 -0.223 0.000 0.947 0.053 0
## 392 0.977 0.431 0.548 0.022 0
## 393 -0.699 0.031 0.831 0.138 0
## 394 0.000 0.000 1.000 0.000 0
## 395 0.000 0.000 1.000 0.000 0
## 396 0.372 0.048 0.952 0.000 0
## 397 0.631 0.235 0.765 0.000 1
## 398 0.382 0.066 0.934 0.000 0
## 399 -0.273 0.059 0.857 0.084 0
## 400 -0.796 0.000 0.679 0.321 0
## 401 0.440 0.083 0.917 0.000 0
## 402 0.103 0.155 0.732 0.113 0
## 403 0.835 0.227 0.677 0.096 1
## 404 -0.542 0.000 0.829 0.171 0
## 405 -0.527 0.121 0.717 0.163 0
## 406 0.000 0.000 1.000 0.000 0
## 407 -0.913 0.054 0.654 0.291 0
## 408 -0.296 0.000 0.916 0.084 0
## 409 0.660 0.167 0.833 0.000 0
## 410 -0.670 0.000 0.879 0.121 0
## 411 0.382 0.073 0.927 0.000 0
## 412 -0.768 0.000 0.885 0.115 1
## 413 0.000 0.000 1.000 0.000 0
## 414 0.625 0.134 0.866 0.000 1
## 415 -0.318 0.000 0.913 0.087 0
## 416 -0.960 0.085 0.565 0.350 0
## 417 0.052 0.109 0.789 0.102 0
## 418 -0.572 0.047 0.791 0.162 0
## 419 0.000 0.000 1.000 0.000 0
## 420 0.000 0.000 1.000 0.000 0
## 421 0.885 0.403 0.493 0.103 1
## 422 0.000 0.000 1.000 0.000 0
## 423 -0.494 0.000 0.775 0.225 0
## 424 0.000 0.000 1.000 0.000 0
## 425 -0.557 0.051 0.836 0.113 0
## 426 -0.359 0.000 0.928 0.072 0
## 427 0.000 0.000 1.000 0.000 0
## 428 0.000 0.000 1.000 0.000 0
## 429 -0.294 0.176 0.564 0.260 0
## 430 0.000 0.000 1.000 0.000 0
## 431 -0.340 0.082 0.784 0.134 0
## 432 -0.856 0.000 0.830 0.170 0
## 433 -0.527 0.000 0.871 0.129 0
## 434 0.000 0.000 1.000 0.000 0
## 435 -0.919 0.000 0.772 0.228 0
## 436 -0.250 0.067 0.800 0.133 0
## 437 0.791 0.389 0.611 0.000 0
## 438 -0.735 0.000 0.881 0.119 0
## 439 0.000 0.000 1.000 0.000 0
## 440 0.751 0.416 0.584 0.000 0
## 441 0.226 0.174 0.826 0.000 0
## 442 0.000 0.000 1.000 0.000 0
## 443 0.000 0.000 1.000 0.000 0
## 444 -0.666 0.121 0.735 0.144 1
## 445 0.927 0.378 0.622 0.000 0
## 446 -0.273 0.000 0.870 0.130 0
## 447 0.000 0.000 1.000 0.000 0
## 448 0.000 0.000 1.000 0.000 0
## 449 -0.511 0.000 0.891 0.109 0
## 450 -0.361 0.045 0.850 0.105 0
## 451 0.000 0.000 1.000 0.000 0
## 452 0.273 0.296 0.704 0.000 0
## 453 0.417 0.166 0.834 0.000 0
## 454 0.237 0.058 0.900 0.042 0
## 455 0.000 0.000 1.000 0.000 0
## 456 0.000 0.000 1.000 0.000 0
## 457 0.292 0.157 0.741 0.102 0
## 458 -0.439 0.000 0.928 0.072 0
## 459 0.000 0.000 1.000 0.000 0
## 460 0.000 0.000 1.000 0.000 0
## 461 -0.273 0.062 0.832 0.107 0
## 462 0.545 0.184 0.816 0.000 0
## 463 -0.439 0.000 0.896 0.104 0
## 464 -0.821 0.000 0.649 0.351 0
## 465 0.457 0.115 0.885 0.000 0
## 466 -0.202 0.132 0.690 0.178 0
## 467 -0.557 0.000 0.735 0.265 0
## 468 0.000 0.000 1.000 0.000 0
## 469 0.824 0.223 0.777 0.000 0
## 470 0.000 0.000 1.000 0.000 0
## 471 -0.822 0.000 0.840 0.160 0
## 472 -0.670 0.000 0.874 0.126 0
## 473 0.635 0.218 0.782 0.000 0
## 474 0.000 0.086 0.828 0.086 0
## 475 0.866 0.196 0.804 0.000 0
## 476 -0.459 0.000 0.906 0.094 0
## 477 0.361 0.238 0.762 0.000 0
## 478 0.000 0.000 1.000 0.000 0
## 479 -0.785 0.052 0.822 0.126 2
## 480 0.000 0.000 1.000 0.000 0
## 481 0.402 0.072 0.928 0.000 0
## 482 0.000 0.000 1.000 0.000 0
## 483 0.000 0.000 1.000 0.000 1
## 484 0.418 0.065 0.935 0.000 1
## 485 0.913 0.400 0.600 0.000 1
## 486 0.645 0.193 0.715 0.091 0
## 487 -0.325 0.000 0.825 0.175 0
## 488 0.000 0.000 1.000 0.000 0
## 489 -0.625 0.000 0.854 0.146 0
## 490 0.101 0.149 0.721 0.130 0
## 491 0.791 0.389 0.611 0.000 0
## 492 0.691 0.121 0.838 0.042 0
## 493 -0.521 0.000 0.851 0.149 0
## 494 0.000 0.000 1.000 0.000 0
## 495 0.471 0.111 0.862 0.027 1
## 496 0.000 0.000 1.000 0.000 0
## 497 0.000 0.000 1.000 0.000 0
## 498 0.477 0.107 0.893 0.000 0
## 499 0.000 0.000 1.000 0.000 0
## 500 -0.440 0.074 0.769 0.157 0Take a look at vader_summary data frame using the View() function in the console and sort by most positive and negative tweets.
Does it generally seem accurately identify positive and negative tweets? Could you find any that you think were mislabeled?
- YOUR RESPONSE HERE
Hutto, C. & Gilbert, E. (2014) provide an excellent summary of the VADER package on their GitHub repository and I’ve copied and explanation of the scores below:
- The
compoundscore is computed by summing the valence scores of each word in the lexicon, adjusted according to the rules, and then normalized to be between -1 (most extreme negative) and +1 (most extreme positive). This is the most useful metric if you want a single unidimensional measure of sentiment for a given sentence. Calling it a ‘normalized, weighted composite score’ is accurate.
NOTE: The compound score is the one most commonly used for sentiment analysis by most researchers, including the authors.
Let’s take a look at the average compound score for our CCSS sample of tweets:
mean(vader_ccss$compound)## [1] -0.014696Overall, does your CCSS tweets sample lean slightly negative or positive? Is this what you expected?
What if we wanted to compare these results more easily to our other sentiment lexicons just to check if result are fairly consistent?
The author’s note that it is also useful for researchers who would like to set standardized thresholds for classifying sentences as either positive, neutral, or negative. Typical threshold values are:
positive sentiment:
compoundscore >= 0.05neutral sentiment: (
compoundscore > -0.05) and (compoundscore < 0.05)negative sentiment:
compoundscore <= -0.05
Let’s give that a try and see how things shake out:
vader_ccss_summary <- vader_ccss %>%
mutate(sentiment = ifelse(compound >= 0.05, "positive",
ifelse(compound <= -0.05, "negative", "neutral"))) %>%
count(sentiment, sort = TRUE) %>%
spread(sentiment, n) %>%
relocate(positive) %>%
mutate(ratio = negative/positive)
vader_ccss_summary## positive negative neutral ratio
## 1 154 168 178 1.090909Not quite as bleak as we might have expected according to VADER! But then again, VADER brings an entirely different perspective coming from the dark side
In a separate R script file, try using VADER to perform a sentiment analysis of the NGSS tweets and see how they compare. Post your working code in the chunk below.
ngss_sample <- read_csv(here("unit-3", "data", "ngss-tweets.csv")) %>%
sample_n(500)## Rows: 8125 Columns: 8## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): text, source
## dbl (4): author_id, id, conversation_id, in_reply_to_user_id
## lgl (1): possibly_sensitive
## dttm (1): created_at##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.vader_ngss <- vader_df(ngss_sample$text)
vader_ngss_summary <- vader_ngss %>%
mutate(sentiment = ifelse(compound >= 0.05, "positive",
ifelse(compound <= -0.05,
"negative", "neutral"))) %>%
count(sentiment, sort = TRUE) %>%
spread(sentiment, n) %>%
relocate(positive) %>%
mutate(ratio = negative/positive)
vader_ngss_summary## positive negative neutral ratio
## 1 361 30 109 0.08310249How do our results compare to the CSSS sample of tweets?
5. COMMUNICATE
In this case study, we focused on the literature guiding our analysis; wrangling our data into a one-token-per-row tidy text format; and using simple word counts and word clouds to compare common words used in tweets about the NGSS and CCSS curriculum standards. Below, add a few notes in response to the following prompts:
One thing I took away from this learning lab:
One thing I want to learn more about:
Congratulations - you’ve completed your first text mining case study! To complete your work, you can click the drop down arrow at the top of the file, then select “Knit top HTML.” This will create a report in your Files pane that serves as a record of your code and its output you can open or share.
If you wanted, you could save the processed data set to your data folder. The write_csv() function is useful for this. The following code is set to not run, as we wanted to ensure that everyone had the data set needed to begin the second learning lab, but if you’re confident in your prepared data, you can save it with the following:
write_csv()Extra Credit (Optional)
Using your own text data or data that you you pulled from Twitter above, try tidying your data into a tidy text format, examining the top words in your dataset, and conducting sentiment analysis with VADER.
If you’d like to use the data we’ve been working with for extra credit, let’s take a quick look at text analysis using bigrams, or tokens consisting of two words.
Bigrams
So far in this lab, we specified tokens as individual words, but many interesting text analyses are based on the relationships between words, which words tend to follow others immediately, or words that tend to co-occur within the same documents.
We can also use the unnest_tokens() function to tokenize our tweets into consecutive sequences of words, called n-grams. By seeing how often word X is followed by word Y, we could then build a model of the relationships between them.
To specify our tokens as bigrams, We do add token = "ngrams" to the unnest_tokens() function and setting n to the number of words in each n-gram. Let’s set n to 2, so we can examine pairs of two consecutive words, often called “bigrams”:
ngss_bigrams <- ngss_tweets %>%
unnest_tokens(bigram,
text,
token = "ngrams",
n = 2)Before we move any further let’s take a quick look at the most common bigrams in our NGSS tweets:
ngss_bigrams %>%
count(bigram, sort = TRUE)## # A tibble: 111,411 × 2
## bigram n
## <chr> <int>
## 1 https t.co 6240
## 2 ngsschat https 721
## 3 of the 630
## 4 in the 531
## 5 ngss https 455
## 6 the ngss 403
## 7 to the 318
## 8 for the 295
## 9 to be 272
## 10 on the 239
## # … with 111,401 more rowsAs we saw above, a lot of the most common bigrams are pairs of common (uninteresting) words as well. Dealing with these is a little less straightforward and we’ll need to use the separate() function from the tidyr package, which splits a column into multiple based on a delimiter. This lets us separate it into two columns, “word1” and “word2,” at which point we can remove cases where either is a stop-word.
library(tidyr)
bigrams_separated <- ngss_bigrams %>%
separate(bigram, c("word1", "word2"), sep = " ")
bigrams_filtered <- bigrams_separated %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word)
tidy_bigrams <- bigrams_filtered %>%
unite(bigram, word1, word2, sep = " ")Let’s take a look at our bigram counts now:
tidy_bigrams %>%
count(bigram, sort = TRUE)## # A tibble: 45,507 × 2
## bigram n
## <chr> <int>
## 1 https t.co 6240
## 2 ngsschat https 721
## 3 ngss https 455
## 4 ngss ngsschat 236
## 5 ngss aligned 192
## 6 ngss standards 168
## 7 ngss science 154
## 8 science education 148
## 9 science standards 112
## 10 teachers https 106
## # … with 45,497 more rowsBetter, but there are still many tokens not especially useful for analysis.
Let’s make a custom custom stop word dictionary for bigrams just like we did for our unigrams. A list is started for you below, but you likely want to expand our list off stop words:
my_words <- c("https", "t.co")Now let’s separate, filter, and unite again:
tidy_bigrams <- bigrams_separated %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word) %>%
filter(!word1 %in% my_words) %>%
filter(!word2 %in% my_words) %>%
unite(bigram, word1, word2, sep = " ")Note that since my_words is just a vector of words and not a data frame like stop_words, we do not need to select the word column using the $ operator.
Let’s take another quick count of our bigrams:
tidy_bigrams %>%
count(bigram, sort = TRUE)## # A tibble: 37,539 × 2
## bigram n
## <chr> <int>
## 1 ngss ngsschat 236
## 2 ngss aligned 192
## 3 ngss standards 168
## 4 ngss science 154
## 5 science education 148
## 6 science standards 112
## 7 ngss_tweeps ngsschat 96
## 8 science ngss 94
## 9 bmsscienceteach ngss_tweeps 92
## 10 approved approach 89
## # … with 37,529 more rowsYour Turn ⤵
Use the code chunk below to tidy and count our bigrams for the CCSS tweets:
ccss_bigrams <- ccss_tweets %>%
unnest_tokens(bigram,
text,
token = "ngrams",
n = 2) %>%
separate(bigram, c("word1", "word2"), sep = " ") %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word) %>%
filter(!word1 %in% my_words) %>%
filter(!word2 %in% my_words) %>%
unite(bigram, word1, word2, sep = " ")
ccss_bigrams %>%
count(bigram, sort = TRUE)## # A tibble: 85,101 × 2
## bigram n
## <chr> <int>
## 1 common core 26735
## 2 core math 8249
## 3 core standards 683
## 4 core education 420
## 5 core curriculum 372
## 6 gt gt 262
## 7 bill gates 252
## 8 grade common 252
## 9 public schools 246
## 10 grade level 233
## # … with 85,091 more rowsWhat additional insight, if any, did looking at bigrams bring to out analysis?
- YOUR RESPONSE HERE
References
Note: Citations embedded in R Markdown will only show upon knitting.
Krumm, A., Means, B., & Bienkowski, M. (2018). Learning analytics goes to school. Routledge. https://doi.org/10.4324/9781315650722
Rosenberg, J. M., Borchers, C., Dyer, E. B., Anderson, D., & Fischer, C. (2021). Understanding Public Sentiment About Educational Reforms: The Next Generation Science Standards on Twitter. AERA Open, 7, 233285842110242. https://doi.org/10.1177/23328584211024261
Silge, J., & Robinson, D. (2017). Text mining with r: A tidy approach. " O’Reilly Media, Inc.". https://www.tidytextmining.com
Wang, Y., & Fikis, D. J. (2017). Common Core State Standards on Twitter: Public Sentiment and Opinion Leaders. Educational Policy, 33(4), 650–683. https://doi.org/10.1177/0895904817723739
---
title: 'Public Sentiment and the State Standards'
subtitle: 'Unit 3 Case Study'
author: "Dr. Shaun Kellogg"
date: "`r format(Sys.Date(),'%B %e, %Y')`"
output:
  html_document:
    toc: true
    toc_depth: 5
    toc_float: yes
    code_folding: show
    code_download: TRUE
bibliography: lit/references.bib
csl: lit/apa.csl
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## 1. PREPARE

Data sources such as digital learning environments and administrative data systems, as well as data produced by social media websites and the mass digitization of academic and practitioner publications, hold enormous potential to address a range of pressing problems in education, but collecting and analyzing text-based data also presents unique challenges. This week, our case study is guided by my colleague Josh Rosenberg's recent article, **Advancing new methods for understanding public sentiment about educational reforms: The case of Twitter and the Next Generation Science Standards.**

We will focus on conducting a very simplistic "replication study" by comparing the sentiment of tweets about the [Next Generation Science Standards](https://www.nextgenscience.org) (NGSS) and [Common Core State Standards](http://www.corestandards.org) (CCSS) in order to better understand public reaction to these two curriculum reform efforts. Specifically, our Unit 3 case study will cover the following topics:

1.  **Prepare**: We'll take a quick look at Dr. Rosenberg's study, load packages we'll need for analysis, and learn how to use Twitter's API to retrieve data for those that created a Twitter Developer account.
2.  **Wrangle**: We focus on basic text mining processes such as text tokenization and stop word removal. Specifically, we will learn how to "tidy text" so we can perform some basic analyses such as retrieving word counts and term frequencies.
3.  **Explore**: In order to see what insight our data provides into answering our research questions, we will calculate some basic simple summary statistics from our tidied text and use data visualization to highlight some of these insights.
4.  **Model:** We learn about sentiment lexicons in our third lab and introduce the {vader} package to model the sentiment of tweets about the NGSS and CCSS state standards in order to better understand public reaction to these two curriculum reform efforts.\
    **Communicate:**

### 1a. Review the Literature

The **Unit 3 Case Study: Public Sentiment and the State Standards** is guided by a recent publication by [@rosenberg2021] *Understanding Public Sentiment About Educational Reforms: The Next Generation Science Standards on Twitter*. This study in turn builds on upon previous work by @wang2017 examining public opinion on the Common Core State Standards (CCSS) on Twitter. For Module 1, we will focus on analyzing tweets about the [Next Generation Science Standards](https://www.nextgenscience.org) (NGSS) and [Common Core State Standards](http://www.corestandards.org) (CCSS) in order to better understand key words and phrases that emerge, as well as public sentiment towards these two curriculum reform efforts.

[![](img/rosenberg.jpg){width="50%"}](https://doi.org/10.1177/23328584211024261)

[Full Paper (AERA Open)](https://journals.sagepub.com/doi/full/10.1177/23328584211024261)

#### Abstract

System-wide educational reforms are difficult to implement in the United States, but despite the difficulties, reforms can be successful, particularly when they are associated with broad public support. This study reports on the nature of the public sentiment expressed about a nationwide science education reform effort, the Next Generation Science Standards (NGSS). Through the use of data science techniques to measure the sentiment of posts on Twitter about the NGSS (*N* = 565,283), we found that public sentiment about the NGSS is positive, with only 11 negative posts for every 100 positive posts. In contrast to findings from past research and public opinion polling on the Common Core State Standards, sentiment about the NGSS has become more positive over time---and was especially positive for teachers. We discuss what this positive sentiment may indicate about the success of the NGSS in light of opposition to the Common Core State Standards.

#### Data Sources

Similar to data we'll be using for this case study, Rosenberg et al. used publicly accessible data from Twitter collected using the Full-Archive Twitter API and the {rtweet} package in R. Specifically, the authors accessed tweets and user information from the hashtag-based \#NGSSchat online community, all tweets that included any of the following phrases, with "/" indicating an additional phrase featuring the respective plural form: "ngss", "next generation science standard/s", "next gen science standard/s".

Data used in this case was pulled using an [Academic Research developer account](https://developer.twitter.com/en/products/twitter-api/academic-research) and the {academictwitter} package, which uses the Twitter API v2 endpoints and allows researchers to access the full twitter archive, unlike the standard developer account. Data includes all tweets from January through May of 2020 and included the following terms: `#ccss`, `common core`, `#ngsschat`, `ngss`. Below is an example of the code used to retrieve data for this lab. **This code is set not to execute and will not run**, but it does illustrate the search query used, variables selected, and time frame. For those that created a standard developer account, we will learn how to use your developer account later in this section to retrieve data from Twitter.

```{r, eval=FALSE}
library(academictwitteR)
library(tidyverse)

ccss_tweets_2021 <-
  get_all_tweets('(#commoncore OR "common core") -is:retweet lang:en',
                 "2021-01-01T00:00:00Z",
                 "2021-05-31T00:00:00Z",
                 bearer_token,
                 data_path = "ccss-data/",
                 bind_tweets = FALSE)

ccss_tweets <- bind_tweet_jsons(data_path = "ccss-data/") %>%
  select(text,
         created_at,
         author_id,
         id,
         conversation_id,
         source,
         possibly_sensitive,
         in_reply_to_user_id)


write_csv(ccss_tweets, here("data", "ccss-tweets.csv"))
```

#### Analysis

The authors determined Tweet sentiment using the Java version of SentiStrength to assign tweets to two 5-point scales of sentiment, one for positivity and one for negativity, because SentiStrength is a validated measure for sentiment in short informal texts (Thelwall et al., 2011). In addition, they used this tool because Wang and Fikis (2019) used it to explore the sentiment of CCSS-related posts. We'll be using the AFINN sentiment lexicon which also assigns words in a tweet to two 5-point scales, in addition to exploring some other sentiment lexicons to see if they produce similar results. We will use a similar approach to label tweets as positive, negative, or neutral using the {Vader} package which greatly simplifies this process.

The authors also used the `lme4` package in R to run a mixed effects model to determine if sentiment changes over time and differs between teachers and non-teachers. We won't try to replicate in this study, but we will take a look at some of their findings from this model in below.

#### **Summary of Key Findings**

1.  Contrasting with sentiment about CSSS, sentiment about the NGSS science education reform effort is overwhelmingly positive, with approximately 9 positive tweets for every negative tweet.
2.  Teachers were more positive than non-teachers, and sentiment became substantially more positive over the ten years of NGSS-related posts.
3.  Differences between the context of the tweets were small, but those that did not include the \#NGSSchat hashtag became more positive over time than those posts that did not include the hashtag.
4.  Individuals posted more tweets during \#NGSSchat chats, the sentiment of their posts was more positive, suggesting that while the context of individual tweets has a small effect (with posts not including the hashtag becoming more positive over time), the effect upon individuals of being involved in the \#NGSSchat was positive.

Finally, you can watch Dr. Rosenberg provide a quick 3-minute overview of this work at [\<https://stanford.app.box.com/s/i5ixkj2b8dyy8q5j9o5ww4nafznb497x\>](https://stanford.app.box.com/s/i5ixkj2b8dyy8q5j9o5ww4nafznb497x){.uri}

### 1b. Define Questions

One overarching question that Silge and Robinson (2018) identify as a central question to text mining and natural language processing, and that we'll explore later in this case study, is the question:

> How do we to **quantify** what a document or collection of documents is about?

The questions guiding the Rosenberg et al. study attempt to quantify public sentiment around the NGSS and how that sentiment changes over time. Specifically, they asked:

1.  What is the public sentiment expressed toward the NGSS?
2.  How does sentiment for teachers differ from non-teachers?
3.  How do tweets posted to \#NGSSchat differ from those without the hashtag?
4.  How does participation in \#NGSSchat relate to the public sentiment individuals express?
5.  How does public sentiment vary over time?

For our text mining case study, we'll use approaches similar to those used by the authors cited above to better understand public discourse surrounding these standards, particularly as they relate to STEM education. We will also try to guage public sentiment around the NGSS, by comparing how much more positive or negative NGSS tweets are relative to CSSS tweets. Specifically, in this case study we'll attempt to answer the following questions:

1.  What are the most frequent words or phrases used in reference to tweets about the CCSS and NGSS?
2.  How does sentiment for NGSS compare to sentiment for CCSS?

### 1c. Load Libraries

#### tidytext 📦

![](images/tidytext.png){width="20%"}

As we'll learn first hand in this module, using tidy data principles can also make many text mining tasks easier, more effective, and consistent with tools already in wide use. The {tidytext} package helps to convert text into data frames of individual words, making it easy to to manipulate, summarize, and visualize text using using familiar functions form the {tidyverse} collection of packages.

Let's go ahead and load the {tidytext} package:

```{r}
library(tidytext)
```

For a more comprehensive introduction to the `tidytext` package, I cannot recommend enough the free and excellent online book, *Text Mining with R: A Tidy Approach* [@silge2017text]. If you're interested in pursuing text analysis using R post Summer Workshop, this will be a go to reference.

#### The vader Package 📦

![](img/vader.jpeg){width="20%"}\

The {vader} package is for the Valence Aware Dictionary for sEntiment Reasoning (VADER), a rule-based model for general sentiment analysis of social media text and specifically attuned to measuring sentiment in microblog-like contexts.

To learn more about the {vader} package and its development, take a look at the article by Hutto and Gilbert (2014), [VADER: A Parsimonious Rule-based Model forSentiment Analysis of Social Media Text](http://comp.social.gatech.edu/papers/icwsm14.vader.hutto.pdf).

Let's go ahead and load the VADER library:

```{r}
library(vader)
```

**Note:** The {vader} package can take quite some time to run on a large datasets like the one we'll be working with, so in our Model section we will examine just a small(ish) subset of tweets.

#### **rtweet 📦 (Optional)**

![](img/rtweet.jpeg){width="20%"}

The {rtweet} package provides users a range of functions designed to extract data from Twitter's REST and streaming APIs and has three main goals:

1.  Formulate and send requests to Twitter's REST and stream APIs.

2.  Retrieve and iterate over returned data.

3.  Wrangling data into tidy structures.

For those that created a Twitter Developer account, load the {rtweet} package that we'll be using to accomplish all three of the goals listed above:

```{r}
library(rtweet)
```

#### Other Packages

Finally, there are a few other packages we'll need to get started. The first two should look familiar while third {wordcloud2} package is handy little package for creating interactive word clouds.

```{r}
library(tidyverse)
library(here)
library(wordcloud2)
```

## 2. WRANGLE

The importance of data wrangling, particularly when working with text, is difficult to overstate. Just as a refresher, wrangling involves the initial steps of going from raw data to a dataset that can be explored and modeled [@krumm2018]. This case study will place a heavy emphasis on preparing text for analysis and in particular we'll learn how to:

a.  **Import Tweets**. First we revisit the familiar `read_csv()` function for reading in our CCSS and NGSS tweets into R. For those of you who created a Twitter Developer Account, we'll also demonstrate the use of the {rtweet} package for downloading data directly from Twitter.
b.  **Restructure Data**. We focus on removing extraneous data using the `select()` and `filter()` functions from {dplyr}, and revisit functions from the [Tidy Your Data Primer](https://rstudio.cloud/learn/primers/4.3) for merging data frames.
c.  **Tidy Text.** Finally, we introduce the {tidytext} package to "tidy" and tokenize our tweets in order to create our data frame for analysis. We also introduce a new join function to remove "stop words" that don't add much value to our analysis.

### 2a. Part 1: Import Tweets from API (Optional)

This section is optional and for those those [setup a Twitter Developer Account and App](https://laser-institute.github.io/laser-sundry/twitter-setup.html#Introduction). The Import Tweets section introduces the following functions from the {rtweet} package for reading Twitter data into R:

-   [`create_token()`](https://cran.r-project.org/web/packages/rtweet/vignettes/auth.html) Sends request to generate authorization tokens for use of the Twitter API.
-   [`search_tweets()`](https://www.rdocumentation.org/packages/rtweet/versions/0.7.0/topics/search_tweets) Pulls up to 18,000 tweets from the last 6-9 days matching provided search terms. 
-   [`search_tweets2()`](https://www.rdocumentation.org/packages/rtweet/versions/0.7.0/topics/search_tweets) Returns data from **multiple** search queries. 
-   [`get_timelines()`](https://www.rdocumentation.org/packages/rtweet/versions/0.7.0/topics/search_tweets) Returns up to 3,200 tweets of one or more specified Twitter users.

#### Authorization methods

Users can create their personal Twitter token in two different ways. THe recommend method is outlined below.

-   Navigate to [developer.twitter.com/en/apps](https://developer.twitter.com/en/apps) and select your Twitter app
-   Click the tab labeled `Keys and tokens` to retrieve your keys.
-   Locate the `Consumer API keys` (aka "API Secret").

![](img/create-app-6.png){width="100%"}

-   Scroll down to `Access token & access token secret` and click `Create`

![](images/create-app-7.png){width="100%"}

-   Copy and paste the four keys (along with the name of your app) into an R script file and pass them along to `create_token()`. Note, these keys are named secret for a reason. I recommend setting up your token in a separate R script than the one that you will eventually share.

```{r, eval=FALSE}
## store api keys (these are fake example values; replace with your own keys)
app_name <- "Text Mining in Education"
api_key <- "afYS4vbIlPAj096E60c4W1fiK"
api_secret_key <- "bI91kqnqFoNCrZFbsjAWHD4gJ91LQAhdCJXCj3yscfuULtNkuu"
access_token <- "9551451262-wK2EmA942kxZYIwa5LMKZoQA4Xc2uyIiEwu2YXL"
access_token_secret <- "9vpiSGKg1fIPQtxc5d5ESiFlZQpfbknEN1f1m2xe5byw7"

## authenticate via web browser
token <- create_token(
  app = app_name,
  consumer_key = api_key,
  consumer_secret = api_secret_key,
  access_token = access_token,
  access_secret = access_token_secret)
```

#### Authorization in future R sessions

-   The `create_token()` function should automatically save your token as an environment variable for you. So next time you start an R session [on the same machine], rtweet should automatically find your token.
-   To make sure it works, restart your R session, run the following code, and again check to make sure the app name and `api_key` match.

```{r}
## check to see if the token is loaded
get_token()
```

That's it!

#### Search Tweets

Since one of our goals for this walkthrough is a very crude replication of the study by Rosenberg et al. (2021), let's begin by introducing the `search_tweets()` function to try reading into R 5,000 tweets containing the NGSS hashtag and store as a new data frame `ngss_all_tweets`.

Type or copy the following code into your R script or console and run:

```{r}
ngss_all_tweets <- search_tweets(q = "#NGSSchat", n=5000)
```

Note that the first argument `q =` that the `search_tweets()` function expects is the search term included in quotation marks and that `n =` specifies the maximum number of tweets

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

View your new `ngss_all_tweets`data frame using the code chunk below and answer the following questions:

```{r}
ngss_all_tweets
```

1.  How many tweets did our query using the Twitter API actually return? How many variables?

    -   

2.  Why do you think our query pulled in far less than 5,000 tweets requested?

    -   

3.  Does our query also include retweets? How do you know?

    -   

#### Remove Retweets

While not explicitly mentioned in the paper, it's likely the authors removed retweets in their query since a retweet is simply someone else reposting someone else's tweet and would duplicate the exact same content of the original.

Let's use the `include_rts =` argument to remove any retweets by setting it to `FALSE`:

```{r}
ngss_non_retweets <- search_tweets("#NGSSchat", 
                                   n=5000, 
                                   include_rts = FALSE)
```

#### Using the OR Operator

If you recall from the [Data Sources] section above, the authors accessed tweets and user information from the hashtag-based \#NGSSchat online community, all tweets that included any of the following phrases, with "/" indicating an additional phrase featuring the respective plural form: "ngss", "next generation science standard/s", "next gen science standard/s".

Let's modify our query using the `OR` operator to also include "ngss" so it will return tweets containing either \#NGSSchat or "ngss" and assign to `ngss_or_tweets`:

```{r}
ngss_or_tweets <- search_tweets(q = "#NGSSchat OR ngss", 
                                n=5000, 
                                include_rts = FALSE)
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

Try including both search terms but excluding the `OR` operator to answer the following question:

```{r}
ngss_both_tweets <- search_tweets(q = "#NGSSchat ngss", 
                                n=5000, 
                                include_rts = FALSE)
```

1.  Does excluding the `OR` operator return more tweets, the same number of tweets, or fewer tweets? Why?

    -   

2.  What other useful arguments does the `search_tweet()` function contain? Try adding one and see what happens.

    -   

**Hint:** Use the `?search_tweets` help function to learn more about the `q` argument and other arguments for composing search queries.

#### Use Multiple Queries

Unfortunately, the `OR` operator will only get us so far. In order to include the additional search terms, we will need to use the `c()` function to combine our search terms into a single list.

The `rtweets` package has an additional `search_tweets2()` function for using multiple queries in a search. To do this, either wrap single quotes around a search query using double quotes, e.g., `q = '"next gen science standard"'` or escape each internal double quote with a single backslash, e.g., `q = "\"next gen science standard\""`.

Copy and past the following code to store the results of our query in `ngss_tweets`:

```{r}
ngss_tweets <- search_tweets2(c("#NGSSchat OR ngss", 
                                '"next generation science standard"', 
                                '"next generation science standards"', 
                                '"next gen science standard"', 
                                '"next gen science standards"' ), 
                              n=5000, 
                              include_rts = FALSE)
```

#### Our First Dictionary

Recall that for our research question we wanted to compare public sentiment about both the NGSS and CCSS state standards. Let's go ahead and create our very first "dictionary" for identifying tweets related to either set of standards, and then use that dictionary for our the `q =` query argument to pull tweets related to the state standards.

To do so, we'll need to add some additional search terms to our list:

```{r}
ngss_dictionary <- c("#NGSSchat OR ngss", 
                     '"next generation science standard"', 
                     '"next generation science standards"', 
                     '"next gen science standard"', 
                     '"next gen science standards"') 

ngss_tweets <- search_tweets2(ngss_dictionary, n=5000, include_rts = FALSE)
```

Now let's create a dictionary for the Common Core State Standards and pass that to our `search_tweets()` function to get the most recent tweets:

```{r}
ccss_dictionary <- c("#commoncore", '"common core"') 
ccss_tweets <- 
  ccss_dictionary %>% 
  search_tweets2(n=5000, include_rts = FALSE)
```

Notice that you can use the pipe operator with the `search_tweets()` function just like you would other functions from the tidyverse.

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

1.  Use the `search_tweets` function to create you own custom query for a twitter hashtag or topic(s) of interest.

#### Other Useful Queries

For your independent analysis, you may be interest in exploring posts by specific users rather than topics, key words, or hashtags. Yes, there is a function for that too!

For example, let's create another list containing the usernames of me and some of my colleagues at the Friday Institute using the `c()` function again and use the `get_timelines()` function to get the most recent tweets from each of those users:

```{r}
fi <- c("sbkellogg", "TooSweetGeek", "haspires", "tarheel93", "drcallie_tweets", "AlexDreier") 

fi_tweets <- fi %>% get_timelines(include_rts=FALSE)
```

And let's use the `sample_n()` function from the `dplyr` package to pick 10 random tweets and use `select()` to select and view just the `screenname` and `text` columns that contains the user and the content of their post:

```{r}
sample_n(fi_tweets, 10) %>% select(screen_name, text)
```

We've only scratched the surface of the number of functions available in the `rtweets` package for searching Twitter. Use the following function to learn more about the {retwee} package:

```{r, eval=F}
vignette("intro", package="rtweet")
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

To conclude Section 2a, try one of the following search functions from the `rtweet` vignette:

1.  `get_timelines()` Get the most recent 3,200 tweets from users.
2.  `stream_tweets()` Randomly sample (approximately 1%) from the live stream of all tweets.
3.  `get_friends()` Retrieve a list of all the accounts a user follows.
4.  `get_followers()` Retrieve a list of the accounts following a user.
5.  `get_favorites()` Get the most recently favorited statuses by a user.
6.  `get_trends()` Discover what's currently trending in a city.
7.  `search_users()` Search for 1,000 users with the specific hashtag in their profile bios.

### 2a. Part 2: Import Tweets from CSV

First, let's use the by now familiar `read_csv()` and `here()` functions to import our `ccss_tweets.csv` file saved in our data folder:

```{r}
ccss_tweets <- read_csv(here("unit-3", "data", "ccss-tweets.csv"), 
          col_types = cols(author_id = col_character(), 
                           id = col_character(),
                           conversation_id = col_character(), 
                           in_reply_to_user_id = col_character()
                           )
          )
```

Note the addition of the `col_types =` argument for changing some of the column types to character strings because the numbers for those particular columns actually indicate identifiers for authors and tweets:

-   `author_id` = the author of the tweet

-   `id` = the unique id for each tweet

-   `converastion_id` = the unique id for each [conversation thread](https://developer.twitter.com/en/docs/twitter-api/conversation-id)

-   `in_reply_to_user_id` = the author of the tweet being replied to

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

Use the following code chunk to import the NGSS tweets located in the same data folder as our common core tweets. By default, R will treat numerical IDs in our dataset as numeric values but we will need to convert these to characters like demonstrated above for the purpose of analysis:

```{r}
ngss_tweets <- read_csv(here("unit-3", "data", "ngss-tweets.csv"), 
          col_types = cols(author_id = col_character(), 
                           id = col_character(),
                           conversation_id = col_character(), 
                           in_reply_to_user_id = col_character()
                           )
          )
```

Importing data and dealing with data types can be a bit tricky, especially for beginners. Recall from previous case studies that RStudio has an "Import Dataset" feature in the Environment Pane that can help you use the {readr} package and associated functions to greatly facilitate this process. If you get stuck, you can copy the code generated in the lower right hand corner of the Import Dataset window.

![](images/import-data.png){width="80%"}

Now use the following code chunk to inspect the `head()` of each data frame and answer the questions that follow:

```{r}
head(ngss_tweets)
head(ccss_tweets)
```

Wow, so much for a family friendly case study! Based on this very limited sample, which set of standards do you think Twitter users are more negative about?

-   CSSS

Let's take a slightly larger sample of the CCSS tweets:

```{r}
ccss_tweets %>% 
  sample_n(20) %>%
  relocate(text)
           
```

#### [Your Turn]{style="color: green;"} ⤵

Use the code chunk below to take a sample of the NGSS tweets. Try to do it without looking at the code above first:

```{r}
ngss_tweets %>% 
  sample_n(20) %>%
  relocate(text)
```

1.  Still of the same opinion?

    -   Yes, these appear much less negative.

2.  What else you notice about our data sets? Record a few observations that you think are relevant to our analysis or might be useful for future analyses.

    -   

3.  What questions do you have about these data sets? What are you still curious about?

    -   

### 2b. Restructure Data

#### Subset Tweets

As you may have noticed, we have more data than we need for our analysis and should probably pare it down to just what we'll use.

Let's start with the CCSS tweets first. And since this is a family friendly case study, let's use the `filter()` function introduced in previous labs to filter out rows containing "possibly sensitive" language:

```{r, eval=TRUE}
ccss_tweets_1 <- ccss_tweets %>% 
  filter(possibly_sensitive == "FALSE")
```

Now let's use the `select()` function to select the following columns from our new `ss_tweets_clean` data frame:

1.  `text` containing the tweet which is our primary data source of interest
2.  `author_id` of the user who created the tweet
3.  `created_at` timestamp for examining changes in sentiment over time
4.  `conversation_id` for examining sentiment by conversations
5.  `id` for the unique reference id for each tweet and useful for counts

```{r select-variables, eval=TRUE}
ccss_tweets_2 <- ccss_tweets_1 %>% 
  select(text,
         author_id,
         created_at, 
         conversation_id,
         id)
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

**Note:** The `select()` function will also reorder your columns based on the order in which you list them.

Use the code chunk below to reorder the columns to your liking and assign to `ccss_tweets_3`:

```{r}
ccss_tweets_3 <- ccss_tweets_1 %>% 
  select(id,
         text,
         author_id,
         created_at, 
         conversation_id)
```

#### Add & Relocate Columns

Finally, since we are interested in comparing the sentiment of NGSS tweets with CSSS tweets, it would be helpful if we had a column to quickly identify the set of state standards with which each tweet is associated.

We'll use the `mutate()` function to create a new variable called `standards` to label each tweets as "ngss":

```{r}
ccss_tweets_4 <- mutate(ccss_tweets_2, standards = "ccss")

colnames(ccss_tweets_4)
```

And just because it bothers me, I'm going to use the `relocate()` function to move the `standards` column to the first position so I can quickly see which standards the tweet is from:

```{r}
ccss_tweets_5 <- relocate(ccss_tweets_4, standards)

colnames(ccss_tweets_5)
```

Again, we could also have used the `select()` function to reorder columns like so:

```{r}
ccss_tweets_5 <- ccss_tweets_4 %>% 
  select(standards,
         text,
         author_id,
         created_at, 
         conversation_id,
         id)

colnames(ccss_tweets_5)
```

Before moving on to the CCSS standards, let's use the `%>%` operator and rewrite the code from our wrangling so there is less redundancy and it is easier to read:

```{r}
# Search Tweets
ccss_tweets_clean <- ccss_tweets %>%
  filter(possibly_sensitive == "FALSE") %>%
  select(text, author_id, created_at, conversation_id, id) %>%
  mutate(standards = "ccss") %>%
  relocate(standards)

head(ccss_tweets_clean)
```

#### [Your Turn]{style="color: green;"} ⤵

Recall from section [1b. Define Questions] that we are interested in comparing word usage and public sentiment around both the Common Core and Next Gen Science Standards.

Create an new `ngss_tweets_clean` data frame consisting of the Next Generation Science Standards tweets we imported by using the code above as a guide.

```{r}
ngss_tweets_clean <- ngss_tweets %>%
  filter(possibly_sensitive == "FALSE") %>%
  select(text, author_id, created_at, conversation_id, id) %>%
  mutate(standards = "ngss") %>%
  relocate(standards)

head(ngss_tweets_clean)
```

#### Merge Data Frames

Finally, let's combine our CCSS and NGSS tweets into a single data frame by using the `union()` function from `dplyr` and simply supplying the data frames that you want to combine as arguments:

```{r}
ss_tweets <- union(ccss_tweets_clean,
                   ngss_tweets_clean)

ss_tweets
```

Note that when creating a "union" like this (i.e. stacking one data frame on top of another), **you should have the same number of columns in each data frame and they should be in the exact same order**.

Alternatively, we could have used the `bind_rows()` function from {dplyr} as well:

```{r}
ss_tweets <- bind_rows(ccss_tweets_clean,
                       ngss_tweets_clean)

ss_tweets
```

The distinction between these two functions is that union by default removes any duplicate rows that might have shown up in our queries.

However, since both functions returned the same number of rows, it's clear we do not have any duplicates. If we wanted to verify, {dplyr} also has an intersect function to merge the two data frames, but only where they `intersect()`, or where they have duplicate rows.

```{r}
ss_tweets_duplicate <- intersect(ccss_tweets_clean,
                                 ngss_tweets_clean)
```

#### [Your Turn]{style="color: green;"} ⤵

Finally, let's take a quick look at both the `head()` and the `tail()` of this new `ss_tweets` data frame to make sure it contains both "ngss" and "ccss" standards:

```{r}
head(ss_tweets)
tail(ss_tweets)
```

### 2c. Tidy Text

Text data by it's very nature is ESPECIALLY untidy and is sometimes referred to as "unstructured" data. In this section we learn some very useful functions from the {tidytext} package to convert text to and from tidy formats. Having our text in a tidy format will allow us to switch seamlessly between tidy tools and existing text mining packages, while also making it easier to visualize text summaries in other data analysis tools like Tableau.

#### Tokenize Text {data-link="2b. Tidy Text"}

In Chapter 1 of Text Mining with R, @silge2017text define the tidy text format as a table with one-token-per-row, and explain that:

> A **token** is a meaningful unit of text, such as a word, two-word phrase (bigram), or sentence that we are interested in using for analysis. And tokenization is the process of splitting text into tokens.

This one-token-per-row structure is in contrast to the ways text is often stored for text analysis, perhaps as strings in a corpus object or in a document-term matrix. For tidy text mining, the token that is stored in each row is most often a single word, but can also be an n-gram, sentence, or paragraph.

For this part of our workflow, our goal is to transform our `ss_tweets` data from this:

```{r}
head(relocate(ss_tweets, text))
```

Into a "tidy text" one-token-per-row format that looks like this:

```{r}
tidy_tweets <- ss_tweets %>% 
  unnest_tokens(output = word, 
                input = text) %>%
  relocate(word)

head(tidy_tweets)
```

If you take ECI 588: Text Mining in Education, you'll learn about other data structures for text analysis like the document-term matrix and corpus objects. For now, however, working with the familiar tidy data frame allows us to take advantage of popular packages that use the shared tidyverse syntax and principles for wrangling, exploring, and modeling data.

As demonstrated above, the `tidytext` package provides the incredibly powerful `unnest_tokens()` function to tokenize text (including tweets!) and convert them to a one-token-per-row format.

Let's tokenize our tweets by using this function to split each tweet into a single row to make it easier to analyze and take a look:

```{r unnest-tokens}
ss_tokens <- unnest_tokens(ss_tweets, 
                           output = word, 
                           input = text)

head(relocate(ss_tokens, word))
```

There is A LOT to unpack with this function:

-   First notice that `unnest_tokens()` expects a data frame as the first argument, followed by two column names.
-   The next argument is an output column name that doesn't currently exist but will be created as the text is "unnested" into it, `word` in this case).
-   This is followed by the input column that the text comes from, which we uncreatively named `text`.
-   By default, a token is an individual word or unigram.
-   Other columns, such as `author_id` and `created_at`, are retained.
-   All punctuation has been removed.
-   Tokens have been changed to lowercase, which makes them easier to compare or combine with other datasets (use the `to_lower = FALSE` argument to turn off if desired).

**Note:** Since {tidytext} follows tidy data principles, we also could have used the `%>%` operator to pass our data frame to the `unnest_tokens()` function like so:

```{r}
ss_tokens <- ss_tweets %>%
  unnest_tokens(output = word, 
                input = text)

ss_tokens
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

The `unnest_tokens()` function also has a specialized `“tweets”` tokenizer in the `tokens =` argument that is very useful for dealing with Twitter text. It retains hashtags and mentions of usernames with the \@ symbol as illustrated by our \@catturd2 friend who featured prominently in our the first CCSS tweet.

Rewrite the code above (**you can check answer below**) to include the token argument set to "tweets", assign to `ss_tokens_1`, and answer the questions that follow:

```{r}
ss_tokens_1 <- unnest_tokens(ss_tweets, 
                             output = word, 
                             input = text, 
                             token = "tweets")

head(ss_tokens_1)
```

1.  How many observations were our original `ss_tweets` data frame?

    -   

2.  How many observations are there now? Why the difference?

    -   

Before we move any further let's take a quick look at the most common word in our two datasets. To do so, we'll introduce the easy to use `count()` function from the {dplyr} package.

Like most functions we've introduced, the first argument `count()` expects is a data frame which we provided with the `%>%` operator, followed but the column, in our case `word`, whose values we want to count:

```{r}
ss_tokens_1 %>%
  count(word, sort = TRUE)

```

Well, many of these tweets are clearly about the CCSS and math at least, but beyond that it's a bit hard to tell because there are so many "stop words" like "the", "to", "and", "in" that don't carry much meaning by themselves.

#### Remove Stop Words

Often in text analysis, we will want to remove these stop words if they are not useful for an analysis. The `stop_words` dataset in the {tidytext} package contains stop words from three lexicons. We can use them all together, as we have here, or `filter()` to only use one set of stop words if that is more appropriate for a certain analysis.

Let's take a closer the lexicons and stop words included in each:

```{r, eval=FALSE}
View(stop_words)
```

#### The `anti_join` Function

In order to remove these stop words, we will use a function called `anti_join()` that looks for matching values in a specific column from two datasets and returns rows from the original dataset that have no matches like so:

![](img/anti-join.png)

For a good overview of the different `dplyr` joins see here: <https://medium.com/the-codehub/beginners-guide-to-using-joins-in-r-682fc9b1f119>.

Now let's remove stop words that don't help us learn much about what people are saying about the state standards.

```{r stop-unigrams}
ss_tokens_2 <- anti_join(ss_tokens_1,
                         stop_words,
                         by = "word")

head(ss_tokens_2)
```

Notice that we've specified the `by =` argument to look for matching words in the `word` column for both data sets and remove any rows from the `tweet_tokens` dataset that match the `stop_words` dataset. Remember when we first tokenized our dataset I conveniently chose `output = word` as the column name because it matches the column name `word` in the `stop_words` dataset contained in the `tidytext` package. This makes our call to `anti_join()`simpler because `anti_join()` knows to look for the column named `word` in each dataset. However this wasn't really necessary since `word` is the only matching column name in both datasets and it would have matched those columns by default.

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

Use the code chunk below to take a quick count of the most common tokens in our `ss_tweets_2` data frame to see if the results are a little more meaningful, then answer the questions that follow.

```{r}
ss_tokens_2 %>%
  count(word, sort = TRUE)
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

1.  How many unique tokens are in our data tidied text?

    -   73,596

2.  How many times does the word "math" occur in our set of tweets?

    -   11,788

#### Custom Stop Words

Notice that the nonsense word "amp" is among our high frequency words as well as some. We can create our own custom stop word list to to weed out any additional words that don't carry much meaning but skew our data by being so prominent.

Let's create a custom stop word list by using the simple `c()` function to combine our words. We can the add a filter to keep rows where words in our `word` column do NOT `!` match words `%in%` `my_stopwords` list:

```{r}
my_stopwords <- c("amp", "=", "+")

ss_tokens_3 <-
  ss_tokens_2 %>%
  filter(!word %in% my_stopwords)
```

Let's take a look at our top words again and see if that did the trick:

```{r}
ss_tokens_3 %>%
  count(word, sort = TRUE)
```

Much better! Note that we could extend this stop word list indefinitely. Feel free to use the code chunk below to try adding more words to our stop list.

Before we move any further, let's save our tidied tweets as a new data frame for Section 3 and also save it as a .csv file in our data folder:

```{r}
ss_tidy_tweets <- ss_tokens_3

write_csv(ss_tokens_3, here("unit-3", "data", "ss_tidy_tweets.csv"))
```

## 3. EXPLORE

Calculating summary statistics, data visualization, and feature engineering (the process of creating new variables from a dataset) are a key part of exploratory data analysis. For our first lab, we're going to keep things super simple and focus on:

a.  **Top Tokens.** Since once of our goals is to compare tweets about the NGSS and CSSS standards, we'll take a look at the to 50 words that appear in each.

b.  **Word Clouds.** To help illustrate the relative frequency each of these top 50 words occurs, we'll introduce the {wordclouds2} package for creating interactive word clouds that can be knitted with your HTML doc.

### 3a. Top Tokens

First, let's take advantage of the the `%>%` operator combine some of the functions we've used above with the `top_n()` function from the {dplyr} package. By default, this function is looking for a data frame as the first argument, and then the number of rows to return.

Let's take a look at the top tokens among the CCSS tweets by filtering our `standards` by CCSS, counting the number of times each `word` occurs, and taking the look at the 50 most common words:

```{r}
ccss_top_tokens <- ss_tidy_tweets %>%
  filter(standards == "ccss") %>%
  count(word, sort = TRUE) %>%
  top_n(50)

ccss_top_tokens
```

Not surprisingly, our search terms appear in the top 50 but the word "math" also features prominently among CCSS tweets!

### Word Clouds

Word clouds are much maligned and sometimes referred to as the "pie charts of text analysis", but they can be useful for communicating simple summaries of qualitative data for education practitioners and are intuitive for them to interpret. Also, for better or worse, these are now included as a default visualization for open-ended survey items in online Qualtrics reports and you can even add your own stop words.

The {wordclouds2} package is pretty dead simple tool for generating HTML based interactive word clouds. By default, when you pass a data frame to the `wordcloud2()` function, it will look for a word column and a column with frequencies or counts, i.e., our column `n` that we created with the `count()` function.

Let's run the `wordcloud2()` function on our `ccss_top_tokens` data frame.

```{r}

wordcloud2(ccss_top_tokens)
```

As you can see, "math" is a pretty common topic when discussing the common core on twitter but words like "core" and "common" -- which you can see better if you click the "show in a new window" button or run the code in you console -- are not very helpful since those were in our search terms when pulling data from Twitter.

In fact, search terms like these we might want to exclude from a final data product we share with with education partners or in a publication and instead include these these in a title or caption.

```{r}

ccss_top_tokens %>%
  filter(word != "common" & word != "core") %>%
  wordcloud2()
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

In the code chunk below, filter, count and select the top 50 tokens to create a word cloud for the NGSS tweets. A gold star if you can can do it without using the assignment operator or looking at the code above!

```{r}
ss_tidy_tweets %>%
  filter(standards == "ngss") %>%
  count(word, sort = TRUE) %>%
  top_n(50) %>%
  wordcloud2()
```

Also, take a look at the help file for `wordclouds2` to see if there might be other ways you could improve the aesthetics of this visualization.

## 4. MODEL

Now that we have our tweets nice and tidy, we're almost ready to begin exploring public sentiment (at least for the past week due to Twitter API rate limits) around the CCSS and NGSS standards. For this part of our workflow we introduce two new functions from the [`tidytext`](https://github.com/juliasilge/tidytext) and `dplyr` packages respectively:

#### How do you "measure" sentiment?

Sentiment analysis tries to evaluate words for their emotional association. In [Text Mining with R: A Tidy Approach](https://www.tidytextmining.com/sentiment.html), Silge aand Robinson point out that,

> One way to analyze the sentiment of a text is to consider the text as a combination of its individual words and the sentiment content of the whole text as the sum of the sentiment content of the individual words.

This isn't the only way to approach sentiment analysis, but it is an easier entry point into sentiment analysis and you'll find that is it often-used in publications that utilize sentiment analysis.

The {tidytext} package provides access to several sentiment **lexicons,** [sometimes referred to as dictionaries](https://cbail.github.io/textasdata/dictionary-methods/rmarkdown/Dictionary-Based_Text_Analysis.html), based on unigrams, i.e., single words. These lexicons contain many English words and the words are assigned scores for positive/negative sentiment, and also possibly emotions like joy, anger, sadness, and so forth.

The three general-purpose lexicons we'll focus on are:

-   `AFINN` assigns words with a score that runs between -5 and 5, with negative scores indicating negative sentiment and positive scores indicating positive sentiment.

-   `bing` categorizes words in a binary fashion into positive and negative categories.

-   `nrc` categorizes words in a binary fashion ("yes"/"no") into categories of positive, negative, anger, anticipation, disgust, fear, joy, sadness, surprise, and trust.

Note that **if this is your first time using the AFINN and NRC lexicons, you may prompted to download both** Respond yes to the prompt by entering "1" and the NRC and AFINN lexicons will download. You'll only have to do this the first time you use the NRC lexicon.

Let's take a quick look at each of these lexicons using the `get_sentiments()` function and assign them to their respective names for later use:

```{r}
afinn <- get_sentiments("afinn")

afinn
```

```{r}
bing <- get_sentiments("bing")

bing
```

```{r}
nrc <- get_sentiments("nrc")

nrc
```

And just out of curiosity, let's take a look at the `loughran` lexicon as well:

```{r}
loughran <- get_sentiments("loughran")

loughran
```

##### ✅ Comprehension Check

1.  How were these sentiment lexicons put together and validated? Hint: take a look at Chapter 2 from Text Mining with R.

    -   

2.  Why should we be cautious when using and interpreting them?

    -   

#### Come to the Dark Side

![](img/dark-side.jpeg){width="80%"}

As noted in the PERPARE section, the {vader} package is for the Valence Aware Dictionary for sEntiment Reasoning (VADER), a rule-based model for general sentiment analysis of social media text and specifically attuned to measuring sentiment in microblog-like contexts such as Twitter.

The VADER assigns a number of different sentiment measures based on the context of the entire social-media post or in our case a tweet. Ultimately, however, these measures are based on a sentiment lexicon similar to those you just saw above. One benefit of using VADER rather than the approaches described by Silge and Robinson is that we use it with our tweets in their original format and skip the text preprocessing steps demonstrated above.

One drawback to VADER is that it can take a little while to run since it's computationally intensive. Instead of analyzing tens of thousands of tweets, let's read in our original `ccss-tweets.csv` and take instead just a sample of 500 "untidu" CCSS tweets using the `sample_n()` function:

```{r}
ccss_sample <- read_csv(here("unit-3", "data", "ccss-tweets.csv")) %>%
  sample_n(500)

ccss_sample
```

Note above that we passed our `read_csv()` output directly to our `sample()` function rather than saving a new data frame object, passing that to `sample_n()`, and saving as another data frame object. The power of the `%>%` pipe!

On to the Dark Side. The {vader} package basically has just one function, `vader_df()` that does one thing and expects just one column from one frame. He's very single minded! Let's give VADER our `ccss_sample` data frame and include the `$` operator to include only the `text` column containing our tweets.

```{r}
vader_ccss <- vader_df(ccss_sample$text)

vader_ccss
```

Take a look at `vader_summary` data frame using the `View()` function in the console and sort by most positive and negative tweets.

Does it generally seem accurately identify positive and negative tweets? Could you find any that you think were mislabeled?

-   YOUR RESPONSE HERE

Hutto, C. & Gilbert, E. (2014) provide an excellent summary of the VADER package on their [GitHub repository](scores) and I've copied and explanation of the scores below:

-   The `compound` score is computed by summing the valence scores of each word in the lexicon, adjusted according to the rules, and then normalized to be between -1 (most extreme negative) and +1 (most extreme positive). This is the most useful metric if you want a single unidimensional measure of sentiment for a given sentence. Calling it a 'normalized, weighted composite score' is accurate.

**NOTE:** The `compound` score is the one most commonly used for sentiment analysis by most researchers, including the authors.

Let's take a look at the average compound score for our CCSS sample of tweets:

```{r}
mean(vader_ccss$compound)
```

Overall, does your CCSS tweets sample lean slightly negative or positive? Is this what you expected?

What if we wanted to compare these results more easily to our other sentiment lexicons just to check if result are fairly consistent?

The author's note that it is also useful for researchers who would like to set standardized thresholds for classifying sentences as either positive, neutral, or negative. Typical threshold values are:

-   **positive sentiment**: `compound` score \>= 0.05

-   **neutral sentiment**: (`compound` score \> -0.05) and (`compound` score \< 0.05)

-   **negative sentiment**: `compound` score \<= -0.05

Let's give that a try and see how things shake out:

```{r}
vader_ccss_summary <- vader_ccss %>% 
  mutate(sentiment = ifelse(compound >= 0.05, "positive",
                            ifelse(compound <= -0.05, "negative", "neutral"))) %>%
  count(sentiment, sort = TRUE) %>% 
  spread(sentiment, n) %>% 
  relocate(positive) %>%
  mutate(ratio = negative/positive)

vader_ccss_summary
```

Not quite as bleak as we might have expected according to VADER! But then again, VADER brings an entirely different perspective coming from the dark side

In a separate R script file, try using VADER to perform a sentiment analysis of the NGSS tweets and see how they compare. Post your working code in the chunk below.

```{r}
ngss_sample <- read_csv(here("unit-3", "data", "ngss-tweets.csv")) %>%
  sample_n(500)

vader_ngss <- vader_df(ngss_sample$text)

vader_ngss_summary <- vader_ngss %>% 
  mutate(sentiment = ifelse(compound >= 0.05, "positive",
                            ifelse(compound <= -0.05, 
                                   "negative", "neutral"))) %>%
  count(sentiment, sort = TRUE) %>% 
  spread(sentiment, n) %>% 
  relocate(positive) %>%
  mutate(ratio = negative/positive)

vader_ngss_summary
```

How do our results compare to the CSSS sample of tweets?

-   

## 5. COMMUNICATE

In this case study, we focused on the literature guiding our analysis; wrangling our data into a one-token-per-row tidy text format; and using simple word counts and word clouds to compare common words used in tweets about the NGSS and CCSS curriculum standards. Below, add a few notes in response to the following prompts:

1.  One thing I took away from this learning lab:

    -   

2.  One thing I want to learn more about:

    -   

Congratulations - you've completed your first text mining case study! To complete your work, you can click the drop down arrow at the top of the file, then select "Knit top HTML". This will create a report in your Files pane that serves as a record of your code and its output you can open or share.

If you wanted, you could save the processed data set to your data folder. The `write_csv()` function is useful for this. The following code is set to not run, as we wanted to ensure that everyone had the data set needed to begin the second learning lab, but if you're confident in your prepared data, you can save it with the following:

```{r, eval = FALSE}
write_csv()
```

## Extra Credit (Optional)

Using your own text data or data that you you pulled from Twitter above, try tidying your data into a tidy text format, examining the top words in your dataset, and conducting sentiment analysis with VADER.

```{r}

```

If you'd like to use the data we've been working with for extra credit, let's take a quick look at text analysis using **bigrams**, or tokens consisting of two words.

### Bigrams

So far in this lab, we specified tokens as individual words, but many interesting text analyses are based on the relationships between words, which words tend to follow others immediately, or words that tend to co-occur within the same documents.

We can also use the `unnest_tokens()` function to tokenize our tweets into consecutive sequences of words, called **n-grams**. By seeing how often word X is followed by word Y, we could then build a model of the relationships between them.

To specify our tokens as bigrams, We do add `token = "ngrams"` to the `unnest_tokens()` function and setting `n` to the number of words in each n-gram. Let's set `n` to 2, so we can examine pairs of two consecutive words, often called "bigrams":

```{r ccss-bigrams}
ngss_bigrams <- ngss_tweets %>% 
  unnest_tokens(bigram, 
                text, 
                token = "ngrams", 
                n = 2)
```

Before we move any further let's take a quick look at the most common bigrams in our NGSS tweets:

```{r}
ngss_bigrams %>% 
  count(bigram, sort = TRUE)
```

As we saw above, a lot of the most common bigrams are pairs of common (uninteresting) words as well. Dealing with these is a little less straightforward and we'll need to use the `separate()` function from the `tidyr` package, which splits a column into multiple based on a delimiter. This lets us separate it into two columns, "word1" and "word2", at which point we can remove cases where either is a stop-word.

```{r stop-bigrams}
library(tidyr)
bigrams_separated <- ngss_bigrams %>%
  separate(bigram, c("word1", "word2"), sep = " ")

bigrams_filtered <- bigrams_separated %>%
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word)

tidy_bigrams <- bigrams_filtered %>%
  unite(bigram, word1, word2, sep = " ")
```

Let's take a look at our bigram counts now:

```{r}
tidy_bigrams %>% 
  count(bigram, sort = TRUE)
```

Better, but there are still many tokens not especially useful for analysis.

Let's make a custom custom stop word dictionary for bigrams just like we did for our unigrams. A list is started for you below, but you likely want to expand our list off stop words:

```{r}
my_words <- c("https", "t.co")
```

Now let's separate, filter, and unite again:

```{r}
tidy_bigrams <- bigrams_separated %>%
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word) %>%
  filter(!word1 %in% my_words) %>%
  filter(!word2 %in% my_words) %>%
  unite(bigram, word1, word2, sep = " ")
```

Note that since `my_words` is just a vector of words and not a data frame like `stop_words`, we do not need to select the `word` column using the `$` operator.

Let's take another quick count of our bigrams:

```{r}
tidy_bigrams %>% 
  count(bigram, sort = TRUE)
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

Use the code chunk below to tidy and count our bigrams for the CCSS tweets:

```{r}
ccss_bigrams <- ccss_tweets %>% 
  unnest_tokens(bigram, 
                text, 
                token = "ngrams", 
                n = 2) %>%
  separate(bigram, c("word1", "word2"), sep = " ") %>%
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word) %>%
  filter(!word1 %in% my_words) %>%
  filter(!word2 %in% my_words) %>%
  unite(bigram, word1, word2, sep = " ")

ccss_bigrams %>% 
  count(bigram, sort = TRUE)
```

What additional insight, if any, did looking at bigrams bring to out analysis?

-   YOUR RESPONSE HERE

### References

Note: Citations embedded in R Markdown will only show upon knitting.
